{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2809ed7-ede4-4008-8b85-4605b49e883e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pylab import *\n",
    "from matplotlib.font_manager import FontProperties  \n",
    "import matplotlib.pyplot as plt  \n",
    "#支持中文\n",
    "mpl.rcParams['font.sans-serif'] = ['SimSun']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4735b2cd-6f2e-4289-aeab-65aa3e76d34c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['25cm风速.xlsx',\n",
       " '30cm风速.xlsx',\n",
       " '40cm风速.xlsx',\n",
       " '50cm风速.xlsx',\n",
       " '60cm风速.xlsx',\n",
       " '70cm风速.xlsx',\n",
       " '80cm风速.xlsx',\n",
       " '90cm风速.xlsx',\n",
       " '100cm风速.xlsx']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list0 = os.listdir()\n",
    "list1 = list0[1:10]\n",
    "list1.sort(key=lambda x:int(x[:-9]))\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45606a17-7e7e-4b30-8b6b-77609b9f4fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_cal1(list1):\n",
    "    mean1 = []\n",
    "    for i in list1:\n",
    "        dat = pd.read_excel(i,header = 0)\n",
    "        dat.columns = np.arange(4)\n",
    "        mean1.append(dat[1].mean())\n",
    "    return mean1\n",
    "def mean_cal2(list1):\n",
    "    mean2 = []\n",
    "    for i in list1:\n",
    "        dat = pd.read_excel(i,header = 0)\n",
    "        dat.columns = np.arange(4)\n",
    "        mean2.append(dat[3].mean())\n",
    "    return mean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ba705da-e196-4ca8-8875-5a2b11d61c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.26077412925,\n",
       " 5.4956240035,\n",
       " 5.720651832000001,\n",
       " 5.895184457250001,\n",
       " 6.156943771500001,\n",
       " 6.453054957250001,\n",
       " 6.52337859575,\n",
       " 6.673751314499999,\n",
       " 6.67083078775]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean1 = mean_cal1(list1)\n",
    "mean1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e174197d-cd3b-412f-86ac-a6e11252ae5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.34771175125,\n",
       " 5.5366356,\n",
       " 5.7329385145,\n",
       " 5.981945689,\n",
       " 6.192481201499999,\n",
       " 6.404512016000001,\n",
       " 6.4537306915,\n",
       " 6.6166583075,\n",
       " 6.618308494999999]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean2 = mean_cal2(list1)\n",
    "mean2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27a94685-4089-478e-8424-a10b8eabedf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19999.500000\n",
       "1        5.260774\n",
       "2    19999.500000\n",
       "3        5.347712\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1 = pd.read_excel('25cm风速.xlsx',header = 0)\n",
    "dat1.columns = np.arange(4)\n",
    "dat1\n",
    "dat1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fbfcbba-af2d-49ed-a43d-262a5edef7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19999.500000\n",
       "1        5.495624\n",
       "2    19999.500000\n",
       "3        5.536636\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2 = pd.read_excel('30cm风速.xlsx',header = 0)\n",
    "dat2.columns = np.arange(4)\n",
    "dat2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb1fbaa9-4280-4225-ad86-af1ef1c4b168",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8544457 , 0.8925896 , 0.92913823, 0.95748551, 1.        ,\n",
       "       1.04809386, 1.0595157 , 1.08393897, 1.08346463])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean1/mean1[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4e06a9d-97d7-498c-b70e-023a754ff4d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.25\n",
       "1    0.30\n",
       "2    0.40\n",
       "3    0.50\n",
       "4    0.60\n",
       "5    0.70\n",
       "6    0.80\n",
       "7    0.90\n",
       "8    1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = [0.25,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.00]\n",
    "X1 = pd.Series(x1)\n",
    "S1 = pd.Series(mean1)\n",
    "S2 = pd.Series(mean2)\n",
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9f14906-9d90-4b5c-898a-2bec8a60e19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 6))\n",
    "plt.plot(S1/S1[4],X1/X1[4],marker = '*',markersize = 10,color = 'b')\n",
    "plt.plot(S2/S2[4],X1/X1[4],marker = 'o',markersize = 10,color = 'r')\n",
    "h = 0.6\n",
    "α = 0.15\n",
    "H = np.arange(0.01,1.5,0.01)\n",
    "v =8\n",
    "V = v*(H/h)**α\n",
    "plt.plot(V/8,H/0.6,color = 'black')\n",
    "plt.grid()\n",
    "plt.xlabel('${U/U_{hub}}$',fontsize= 14)\n",
    "plt.ylabel('${Z/H}$',fontsize= 14)\n",
    "plt.xlim(0,1.5)\n",
    "plt.ylim(0,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60212143-7065-408b-adb2-2bc0518c9f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 计算理论廓线（只计算一次）\n",
    "h, α, v = 0.6, 0.15, 8\n",
    "H = np.arange(0.01, 1.5, 0.01)\n",
    "V = v * (H/h)**α\n",
    "\n",
    "# 绘图函数\n",
    "def plot_wind_profile(x_data, y_data, marker, color, title):\n",
    "    plt.figure(figsize=(4, 6))  # 保持原始尺寸\n",
    "    plt.plot(x_data/x_data[4], y_data/y_data[4], marker=marker, markersize=10, color=color)\n",
    "    plt.plot(V/8, H/0.6, color='black')  # 绘制理论曲线\n",
    "    plt.grid()\n",
    "    plt.xlabel('${U/U_{hub}}$', fontsize=14)\n",
    "    plt.ylabel('${Z/H}$', fontsize=14)\n",
    "    plt.xlim(0, 1.5)\n",
    "    plt.ylim(0, 2)\n",
    "    plt.title(title, fontsize=12)  # 添加标题\n",
    "    plt.tight_layout()  # 确保布局完整\n",
    "\n",
    "# 分别绘制两张图\n",
    "plot_wind_profile(S1, X1, '*', 'b', '第一组数据拟合曲线')\n",
    "plot_wind_profile(S2, X1, 'o', 'r', '第二组数据拟合曲线')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e27633b-77a6-498b-ad7d-c85ffd5b30d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>探针1</th>\n",
       "      <th>探针2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.260774</td>\n",
       "      <td>5.347712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5.495624</td>\n",
       "      <td>5.536636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.720652</td>\n",
       "      <td>5.732939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>5.895184</td>\n",
       "      <td>5.981946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>6.156944</td>\n",
       "      <td>6.192481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>6.453055</td>\n",
       "      <td>6.404512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>6.523379</td>\n",
       "      <td>6.453731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>6.673751</td>\n",
       "      <td>6.616658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.670831</td>\n",
       "      <td>6.618308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          探针1       探针2\n",
       "25   5.260774  5.347712\n",
       "30   5.495624  5.536636\n",
       "40   5.720652  5.732939\n",
       "50   5.895184  5.981946\n",
       "60   6.156944  6.192481\n",
       "70   6.453055  6.404512\n",
       "80   6.523379  6.453731\n",
       "90   6.673751  6.616658\n",
       "100  6.670831  6.618308"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(mean1)\n",
    "df2 = pd.DataFrame(mean2)\n",
    "df = pd.concat([df1,df2],axis = 1)\n",
    "df.columns = ['探针1','探针2']\n",
    "df.index = [25,30,40,50,60,70,80,90,100]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7a28f38-c4f7-4e57-ac2d-9efcde51f16f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "可用的中文字体:\n",
      "SimHei\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "\n",
    "# 获取所有可用字体\n",
    "fonts = fm.findSystemFonts()\n",
    "chinese_fonts = [f for f in fonts if 'simhei' in f.lower() or 'microsoftyahei' in f.lower()]\n",
    "\n",
    "print(\"可用的中文字体:\")\n",
    "for font in chinese_fonts:\n",
    "    print(fm.FontProperties(fname=font).get_name())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "162d85b1-9b2b-4f37-8621-f59caa52fea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h8t7T3rBhQ77//ntuv/12PvroI4KCgjCZTIwePdo+Dkezs584cYI+ffrYbxEYNGgQL7zwQokyK8ymTZt4/fXX8ff359lnn2XLli2kpqbSrVs3qlevztmzZ6lSpYrD+7R1Op3DzyI5OZkvv/wSuLrUX9OmTe1F9/Lly2nVqhXLly/noYceKvL+8LLQrFkz3n//fRo1akSDBg149NFHadGiBd27d0dRFE6dOgXAunXrWLhwIenp6WRlZXHw4EF27tzJH3/8wZ9//skff/xBq1atyqRPQgihZlJ0CyGEhl37i/8///xDWFiYveDOysri6NGj9l/u8yrsDNVnn32GTqfjrbfeonfv3vYi0nZJbd57axs0aGC/j9zb2xsvLy/78lBvvPEGQ4cO5fjx4/mWRMq73nJycjLVqlXj008/zbccV2F+/PFHHn30Ue666y6mTJliL7r1ej2LFi0iLS2Nd955h9zcXN57771iXw+gVatW7Nu3z+Fzf//9N7feemuxr9G1a9cSnRGHogsn22tUrVrVfobWdib22mWhFEXB09OTmjVrFvp6iqLYl06zvbftEnTb5eu2qw2io6Np3rw569at47bbbsu35JS/vz8///wz06ZNo3fv3vluVbBYLEXeu12YtLS0fPt1796dCRMmMGHCBGrXrs3w4cPtZ2j1er3Dz9ff35+nn34ab29vnn/+ec6fPw9cPaBgu+c+rwULFuR7fO1BkP/7v/8jLS2N559/nuDgYLZv305YWBhNmzblwoULNGvWjHfeeafYWwTyioyMLHB7h+0zABg7diwff/xxiV+vLLz44osALFmyhJMnT/Lbb78B1n8zjh07RmxsLL///jsAvXv35vz58/aDcS1btmT06NH2fIQQorKTolsIISqJ3NxcvvvuO5KTk1mxYgUPP/wwu3fvxmw2F7tm77lz51i+fDlgvez8q6++4vXXX+fDDz8ssO3w4cPzrX0N1hnG586dC1jX9jUajbi5uWE0GsnIyCAmJsZ+OXFhaxYXZ/bs2TzzzDN06dKFVatWFTho4O7uzsqVK7njjjuYPn06bm5u+SaIK4y3tzd33HFHvlmt9+3bx8CBA/MtEebIgAED+P77769rPJB/Yiu4WoTlnfjMVhQ7+tyysrKKfP28BzhsbAdqUlNT8y3tlZdery/w+Xp7e/Pmm2+i0+nyzSNw/vz5fBP15X2uKKmpqfkm/APrgZo1a9awb9++fPfbu7u7Oyy6AwICeO655wB46aWX7AeIunbtar/vOTY2lr59+2KxWLj//vt55plnqFGjhv2MtKP1v6dOncq5c+d4/PHHqVq1qv2S+uDgYG655RYmT57M4MGDC/T/ehV14MSRa8+El3QitWvFxcXx7LPPMnPmTPvP+v79++nUqRN+fn60aNECX19f2rZty0svvUSLFi2IjY2lQYMGMnO5EELkIUW3EEJUEsuWLePChQvcf//9PPLII+Tk5LBr1y50Oh3PP/88Fy9eZPLkyQX2e+2113jvvffs9xP/9ddfNG7cmLZt2zJ06FC8vLxYuXIlEydOxGKxUK1aNSIjI1m6dClgvSQ6732xH374Yb4CFq4WjB07dmTbtm2lGldWVhbjxo3jyy+/pE+fPqxcubLQ+6K9vb1ZvXo1t956K1OnTsXb29t+L21h9Ho9VatWzTfxlW2G6+ImgvPy8qJx48b8/PPPpRrTli1bGDJkSIFLkZOTk9HpdAQEBJCeno5Op2Ps2LH2y6yvVdxyUtnZ2aSkpNjPshqNRvtzqampDgvOosycOZMTJ07w0EMP2dvOnDlDs2bN7I+LOxBgc/r0afsM7TYXLlzg5MmTgHVt7nfffRewfs4leV3bVQQ+Pj5ERkaSk5PD8OHDCQ0NJTIyEl9fX/vl5z/99JPDJfdsZs+ezX///ceWLVu4++677e1TpkyhVatWzJo1i5deeqlEYy1r1zuRWl6//voro0aNws/PD4PBwKeffsq5c+cYPnw4Bw4coGnTpuh0Ovr06YOnpyfdunXj1KlT9OnTBz8/P37//Xfq169fcYMWQggXJrNbCCFEJWA0Gnn11Vfp27cvq1at4s477yQhIYHly5dz//3388EHHzBlyhQGDBhgP3Nq06pVK7p06cLChQuBq4VcaGgozZo1o379+ixbtox+/fphMBi466672LVrF4qiEBkZyc0338xNN91kf70XXniBQ4cOcfToUe6//346dOjA0aNHiYmJKXBpb3EOHDhAq1at+PLLLxk5ciSrV68ucvkzsJ79XLt2LbVr1+aNN97gwIEDRW5vO9jgSHFXCNgu146MjCQyMpI6depQo0aNQv9EREQQGRlJ7dq17fvnde7cOWrVqoVer2fIkCH4+Pjg7+9PUFCQwz++vr72idQcuXz5MqtXr6Zx48Y0btyYH3/80Z7/sWPHHF5+XZR58+axZs0azGYznp6epKenc+DAARo0aICHhwc1a9a0T7AFFHnJ/alTp/IVbZmZmdx///3UqlWLcePG8fHHH3Ps2DEAatSoQWJiosP7ugtjMpl4/PHH2blzJytWrLBPGGY7cNCuXTv7ZGKOZGdnEx8fD1gLbduYWrZsSdeuXfnggw8cXklQEWw/b5GRkfkmUrP9DMLVidQiIyMdXga+efNmTp06xYkTJxg7dizz589n586dhISE0KxZM/vZ9M6dO7NhwwaOHj1K7969yc7O5qOPPpKCWwgh8pCiWwghKoFBgwZx+fJlpk+fjk6n46effsLNzY3z58/z4osvMn78eGbOnMlvv/3G4cOH8+17zz33sHHjxnzLfuU1Z84cDhw4YJ8V/LbbbiM0NJSxY8c63D4sLIzGjRsTGRlJ1apV8fb2thfn166JXBzbRE+LFi1i9uzZDievKqwPP/74IwsWLKB58+ZFbms2m0lNTSUmJsb+58SJE4Djy7PzuvYy39dee41q1aoV+qe4tbxjY2OJiIgArPfWnzx5khkzZuDt7c2WLVu4ePEiFy9eJD4+nrp169KhQ4dCz7aazWYuX77MpEmTUBTF/sd27250dDQtWrQosj95bdiwgf379/P666/TpUsXsrKyiI2NJScnh44dO9K6dWvOnTtH06ZN7TPqF1V07927134FRFZWFgMGDCA2Npaff/6Zt99+m2rVqtnX6K5duzYWi8VehBfn/Pnz3HnnnXz33XcsXryYTp062Q8GdOvWje3bt+Pv70+PHj3st0Vc6/XXXyclJYUFCxawefNm+/wBYJ3Ff82aNcVOmleYrVu32u+pdpYnn3ySHTt2kJ6ezvnz59m9eze///57gUvmH3zwQZKTk2nVqhUXLlzgjz/+4N5773VSr4UQwjVJ0S2EEJXAE088wYwZM2jSpAlgXbprypQp9OnTx76e7pgxY9i/f3+Bpag8PT0LnVQtNjaWl156iUceeYSOHTsC1rOzb7zxBr/88ovDy9ULc/DgQdq2bVtgFu6iuLu7s3r1ah577LES72PTvn37Eu2XmZnJ77//bj8b3LhxYwYOHAhY1/IuyrVFd40aNejSpUu+IldRFFavXg0Ufzn4v//+S+vWrQEICQkhLCyMZ599ls6dOzNq1Ch0Oh3VqlXjhRdeICsri2+//TbfxHZ5xcXFoSiKw7PZhw8f5tixY/Ts2bPI/uT19ttv06BBAx555BF729y5c6levTpRUVE0b97cnm2XLl344osvCu1bfHw8Z86coWvXrpw5c4Y77riDf/75h59++on69evj4+PDpEmT+P7779m1axdNmzYFYMeOHVy+fLnIM94nT56kdevW7Nixg++++44BAwYA1isabPtFRESwZcsW2rVr57CQX716NZ988gnTpk3j8ccf5+mnn8531cTtt99O+/btS/zZ2URFRdGvXz9uueUWli1bVuS2v/76KytWrChwZUpZqVu3Lu3bt8fLy4vY2FhWrFjBCy+8wLBhw/JtFxMTg8FgICMjg8WLF9vHXdQVIkIIUdlI0S2EEJXAgAEDGDlypP1xYmIizZs3L3AfZ2kuCT19+jS9e/ematWqBe7RHj58OCNGjODNN9/kf//7n32N72vZLs+eP38+HTp0ICsrq8T3/FaUAwcOFCiSbX+KK0qvnTTs0qVLbN682b6utO2Pbc3lopYLO3jwIAkJCXTv3j1fu16vZ9myZWRkZHDrrbdy33338ddff/HLL78UWDs5r02bNgE4XHpt3rx5BAUF5ZvErSirVq3in3/+4bXXXrNfEr9p0ybWrFnD6NGj6dOnD2fOnOGhhx7CbDbTvHlznnrqqUKL7uXLl+Pl5UXXrl05dOgQMTEx/Prrr3Tt2tW+zRNPPEH9+vU5dOgQERERBAUFsX79elavXk3Xrl05ffq0fVuj0Uhubi4//PADer2eDRs2sG3bNu655x77NralumwCAwPZuHGj/b7xvJ/boEGDeOihh+yTuU2dOpVq1aoxbty4En1eeZ0/f56kpCR69+5N+/btiYqK4t133803m7mjyefmz5/PwIEDC51Z/0ZkZGRwzz330KhRI6pUqUKDBg14+umnOXTokH0JsPT0dJ577jkGDBhAr1698PPz46OPPrJ/p9u1a1dg8jYhhKisZCI1IYTQsMLuOe7QoYO96LpWQkICUHDJsLzF8N9//82gQYNIS0tj48aNBSa8AutEU9WqVePDDz8kPT2d1atXF7jcNj4+nh07dvD3338zYMAA5s6da7+39kYpioLZbC7VGbeSzqx9rcIuM7/283/nnXeYNGlSoa9jm/E5NTW1wHOff/45Xl5e3H777QWey8rK4o477uCLL77g0KFD9O/fn8zMTLKzswu9xHnt2rX2y/PzOnXqFJ9//jnPPfdcvnvKC/tZunDhAmPGjKF27dr2s9xGo5FRo0YREhLCyy+/jK+vL5988gkjR45k6tSpvP766/b9k5KS8l0RkJ2dzVdffcV9992Hr68vvXr14tixY1StWjXf+3p4eHDw4EH7+O69916WLl3Kf//9x969e+3bX7hwgT59+pCRkcH+/ftp3bo1Y8eOZcSIEfle7+uvvy5wZYKHhwcWi4WEhARuuukmVqxYwfDhw+nQoUO++Qf8/f2ZO3cuDRs2LPD55ObmkpKS4vBqEaPRyK5du8jMzOTw4cPMnDmTESNG2K94MJvNuLu788MPP3DzzTfbX+P8+fP8/PPPNGjQwH6lSt6MbnT28ipVqhAWFkZSUhLDhg3j7rvvtt/HfeHCBd577z0+/vhjLl++zEcffcS4ceP46aefGDBgAP369aNDhw7s2bOHs2fPOnx9IYSobKToFkIIDSvp2tDp6ek89NBDpKSkcPjwYXQ6HTfffDNgLQA7derE6dOnqVq1KjVr1uTixYsEBgayatUq++XoFosFs9lsf0+9Xs/06dPp378/LVq0KFD8paSksH37dsxmM++++y4TJkwo0C9boV/SceRlK4RLs29KSkqp3iM5OZkRI0bYz0peu0zStYWqt7d3kZeQZ2Vl0atXLw4dOgRYizmwFmfffPMNAwYMwNPTk1WrVnH27FkOHjzIpk2bOHjwII0aNeK9997Dw8ODBQsW0LVrV/skbuHh4QQGBjJ+/Hhat27NwYMH+f777+0zWtvk5uYyZMgQ3N3dGT9+fL7nbJ/ntZdu63Q62rZtS+fOnXF3d8dkMvHAAw9w6NAhVq1aZZ/YbsSIESxYsICtW7eiKAqbN2/m4Ycf5syZM/nuAf7www85fvw4ixYtsrddW3Db5P2ZevHFF1mwYAE7duzgkUcewc/Pj/Pnz9OzZ09iYmJ47bXXaNiwIV9//TUTJ05k4sSJNGzYkGbNmhEYGIifnx8Wi8VeJCclJXHy5Eni4uIYPXo006dPJyAggB49evDtt98W+Hnu169fvscnT57koYceIiEhgcuXLxMZGVmg//7+/rzzzjtcvnyZV199tcDPhsFg4JVXXuG9996zXwZvU6tWLebMmZOvwLb9rOedvbwkbLOX5832888/d7jtrFmzmDx5Ml26dOHLL7+0z0x/7733smjRIoYNG8a6deuoW7duofM6CCFEZSNFtxBCaFhJL9X28fEhJCSEAwcO0LFjR4YMGWIvuqtWrUqLFi3o2LEjzz33HN7e3rRv354DBw7k+4Xfdmn0tfeYduvWzeF7+vn58eabbxIUFJTv0ndH/S/qsuvC2AqQ0lyublsKrKQCAgI4d+4c586do2/fvvb1mm1KO3u1l5cXDRo0YM+ePYwePZqwsDDAWpz9+++/eHp64u3tzc8//8yuXbto27YtTz75JL169bLf1wwwduxYTp8+zd9//82OHTuIjo7m2LFjtGzZErAWzrfcckuBs73btm0jKiqKDz/8kODgYIdjuTbfoKAg1q5da/+c3dzceOGFF+jVq1e+QlGn07Fq1SqCg4PR6XR07tyZNm3aMGrUKJ5++mn7dn379iUuLi7fpeQlcfPNN7N48WIWL17MtGnT7J/bzTffzLPPPmt/j8cff5w9e/bwyy+/sG/fPk6ePEl0dDTp6enk5OSQm5trvzpCp9Ph4eFhv43gzjvv5M477yxRf2666SZq1Khhn/Bt1KhRDrcr7pL0t99+mzfffDPfd0Cn0zlcA9yWgaMCvyi22ctL8l1544036Nq1K7169Srw3KBBg2jbti0//PADAwcOLLOrVoQQQu10yvVeSyeEEEIITCZTiWdNL4m0tDQ8PT0Lvd+5rCiKUuAyZIDjx48XOlO9GuXm5pb7ZymEEEIURYpuIYQQQgghhBCinMjs5UIIIYQQQgghRDmRolsIIYQQQgghhCgnUnQLIYQQQgghhBDlpNLNXm6xWDhz5gxVq1Z1OIGMEEIIIYQQQghRHEVRSE1NpXbt2uj1hZ/PrnRF95kzZ+xLsAghhBBCCCGEEDfi1KlThIaGFvp8pSu6q1atClg/GD8/Pyf3pvIymUzs27ePli1blulSO6L8SXbqJvmpl2SnXpKdukl+6iXZqZdasktJSSEsLMxeYxam0i0ZlpKSgr+/P0ajUYpuIYQQQgghhBDXpaS1pUykJpxCURSSk5OpZMd8NEGyUzfJT70kO/WS7NRN8lMvyU69tJadFN3CKcxmMzExMZjNZmd3RZSSZKdukp96SXbqJdmpm+SnXpKdemktOym6hRBCCCGEEEKIciJFtxBCCCGEEEIIUU5cdyo4F2E2m8nNzXV2NzTHbDbj5eVFdnY2JpOpyG3d3d0xGAwV1DNRHJ1Oh7e3t6xzr1KSn3pJduol2amb5Kdekp16aS07mb28EIqicO7cOZKTkyuuc6JQAQEBhISEaOaLJ4QQQgghhFC3ktaWcqa7ELaCOzg4mCpVqkixV8YURcFkMuHm5lbkZ6soChkZGVy4cAGAWrVqVVQXRSEsFguXLl2iRo0a6PVyh4raSH7qJdmpl2SnbpKfekl26qW17KTodsBsNtsL7urVqzu7O5qkKArp6el4eXkVe0DD29sbgAsXLhAcHCyXmjuZxWIhLi6OwMBATfwjWNlIfuol2amXZKdukp96SXbqpbXs1D+CcmC7h7tKlSpO7omwsWUh99cLIYQQQggh1ESK7iLIJeWuQ7IQQgghhBBCqJEU3cJp5DJxddLpdPj7+8uBEJWS/NRLslMvyU7dJD/1kuzUS2vZSdFdScXFxTFixAhSUlKu+zX++ecfsrKyHD6Xnp7OmTNnCt1Xa8sAVCYGg4HGjRvLQROVkvzUS7JTL8lO3SQ/9ZLs1Etr2UnRXUkZjUa+/vpr3NzcOHjwIDqdrtg/n376qX3/1NRU+vfvz9y5cx2+/tKlS2nVqlWh768oCjk5OVSyFes0wWKxkJCQgMVicXZXxHWQ/NRLslMvyU7dJD/1kuzUS2vZSdFdSdmOGnl5eeHl5QVYl0lLTU11+Kd9+/Z4enra9589ezZubm50796dQ4cOsXv3blJTU+3Pe3l52WcdL8yJEyeoVasWx48fL/sBinKjtX8EKxvJT70kO/WS7NRN8lMvyU69tJadFN0VyGxR2HoskZ/2nmbrsUTMloo/y2symUhOTiYnJweAzMxMkpOT8ff3x9fXl4ULF3LixAl8fX2Jiori008/xdfXl7p169qL7qSkJN577z1yc3Pp1q0bnTp1omPHjuzevdv+Praz44VJTU3lscce4/z58+U7YCGEEEIIUemYLQrb4pLYnJDNtrgkp/zeLYSNrNNdQdZFn2Xy6kOcNV69B7qWvxdv9G9Cn2a1KqwfsbGxNG7c2P7Y19eXV155heTkZObPn8/48eNZu3YtTZs25dKlS0yePJmjR4+ydOlS3N3dARgzZgx169Zl27ZtuLmV/kfo0qVL9OrV67r2FUIIIYQQoijX/t79adROp/zeLa6P7YDJ9oRsTIFJdI4MwqBX9zxQUvVUgHXRZ3l68W6uPb52zpjF04t388VjbSrsH4CGDRuSlpbG0aNHad26NVlZWSiKwtKlSxk1ahRLliyhe/fuxMbGcs8997B+/Xr69+9PVlYWS5cuZc6cOaxcuZJly5Zx5MgR9Ho9OTk5+Pv7k5GRAVjPcp89e5bc3FxiYmIAyMnJITg4mJCQEA4ePMitt97KqFGjaNasWYWMW5QdvV5PUFAQer1cKKNGkp96SXbqJdmpm+SnLq70e7coPa0eMNEplWwmq5SUFPz9/TEajfj5+TncJisri/j4eOrVq2e/31lRFDJzzaV+P7NFoddHf3M+Jdvh8zqgpp8X68ffWuojON7uhuue/Ts6OprmzZvbJzL7+eefiY+P5/nnn+f8+fOEhIQQGxtL/fr12bFjBz4+PjRt2pTly5dz8OBBPvjgA0wmEwaDAYPBwJAhQ9ixY4e9EM/NzSUzM9P+GZtMJt566y3Gjx+PxWJBr9dz/Phx6tWrR3x8POHh4UX211EmQgghhBBC2JgtCl3f+zPflaV56YAQfy82vXKb6s+calFhB0xsSbniAZOS1JYgZ7pLLDPXTJNJv5X56yrAuZQsmr/5e6n3PfTWnVTxuLEIp0yZwt13381dd91lv3zc9l8PDw8AOnToAFgL54EDBwLw9ttvM3DgQCIjI5kyZUqB1128eDETJ050OEmaXq9HURSysx0fiBCuzWKx2A+AyFF/9ZH81EuyUy/JTt0kP/XYEZ9UaMEN1t+7zxqzePzr7dT080Kn06HXgV6nQ6+3Xq1puNJmfe7K8/o8f7/meYP+mm11OnQ6MOTZp6jX0tlfs/jXyrttUa9l3b7ga+n1hbyvTodOj+PXuvL38l7m12xRmLz6UIGC25abDpi8+hC9m4So8oCJFN2V0MmTJ+1Lff3xxx8MGDAADw8PPDw80Ol0mEwmAOrVq2e/7zo3N5cnn3ySTz75hJiYGDw9PUlNTSUxMZGYmBgyMzNp2rSpvVAvCdv7CHWxWCxcvHiRunXryi8fKiT5qZdkp16SnbpJfupxPqXwgjuvzccSy7kn2qO75oCD4doC3X4AwMHBCT1Xts9f7Oc9oJCRbSrRAZMd8Ul0rl+94gZeRqToLiFvdwOH3rqz1PvtiE9i6PydxW73zbD2dKgXWOo+ldbu3btp164d1atbf1g3btwIQHZ2Nh4eHixfvpwRI0aQnp5OcHAwH3/8MQ8//DCKomAymTh58iSdOnXC09OTjIwM9Ho9y5YtIysri2PHjlGnTp1S90kIIYQQQojrlZZt4rtdCcz6K7ZE2w/uVJe61atgtihYFLAoCopi/bvZcvXvliv/VRTF4bbW5xUslvzbWhQFc95tLXm2VRxsa8n7+OrzFovicFsl7zaK9bHZwbbXvoZ92yvPl+YmY9t+1pttnXd38oXUkh1YcTVSdJeQTqe7rku5uzUIopa/F+eMWQ5/PG33lnRrUDGz8rVu3ZqXX36ZAQMG2C8bB+ul5BaLhenTp/PYY48xe/Zs+vXrx2effcbDDz+MTqfD3d2d+vXrk5mZCVDk5eVCCCGEEEKUp1NJGSzYcpwVO0+Rmm29glJH4SWh7ffuN+9pqspLlMuaohQs4PMV6FcOFuQt0gsv5q8etCiwraJc2f7qe9ietx1EOHTWyPu/HSm2z8FV1Tm3kxTd5cyg1/FG/yY8vXh3gX8EbF/1N/o3qbAvvk6nY9q0aURHRxd4btasWcTGxrJixQpmz57NsGHD6N69O4sWLWLw4MFFvu7cuXOpX78+PXv2LHFfbPeOC3XR6/WEhobKJXYqJfmpl2SnXpKdukl+rkVRFHbEJzFvczzrD53Htvx2RJAPw7rUw8/LjbHL91q3zbOfM37vdnU62+XeOP/zuLVhEIu3nSz2RGVprwx2FVJ0V4A+zWrxxWNtCqzTHeJC099v27aNV155henTpxMUFARAWFgYEydOZOTIkVgsFoYMGZJvn5ycHE6dOkWfPn3Yu3cvK1euLPH76XS6Ut3/LVyH7ZcPoU6Sn3pJduol2amb5Ocask1mVu87y/zN8Rw8k2Jvv7VhEMO6hNO9QRD6K8W0p5vepX/vFgW52onKsiZFdwXp06wWvZuEsCM+iQupWQRXtR6pcdYPTt6V4jZs2MDDDz/MvffeyzPPPENycrL9uUmTJnH58mWGDh3Kr7/+yrJly+yTre3cuZOEhAQGDhzI4cOHqVatmn0/s9mMxWIp8v2zstR5T0ZlZzabOXLkCA0bNsRgKP28AsK5JD/1kuzUS7JTN8nPuS6mZrNk+wkWbzvJpTTryjde7nrubxPKsFvCaVCzaoF9bL93bzt2kf1HTtCiYV061a+YWznF9VPDicrrJUV3BTLodS4x296ECRP466+/CAwMxGKxMHfuXLp27co333wDWP/nkve/H3/8MV26dKFZs2b25QIMBgM9e/akVatWjB8/vsB7ZGZm2u/9LkxoaCgWi6XclyAQZUtRFIxGY74DN0I9JD/1kuzUS7JTN8nPOQ6eMTJv03FW7ztDjtl6IqeWvxePdw5nUIcwAqoUfcWkQa+jY71ADIlxtHPiiS5ROrYDJltjL7J9/2E6tmhM50j1HzCRorsS8vb2pmbNmkyfPh29Xs+SJUtQFMW+PJitWM57JvqBBx7I9xo6nY6FCxcW+h5PPfUUTz31VDn0XgghhBBCaJHZorDh8HnmbYpne3ySvb31TQEM71KPPs1CcDfIvfVaZ9Dr6BQRiFuSJ+0itHHARIruSuiNN97I9/jaS6VCQ0PlaK4QQgghhKgQKVm5fLvzFAu2HudUkvXkj5tex93NazGsSzitb6pWzCsI4dqk6BZO4+np6ewuiOug1+uJiIiQWVxVSvJTL8lOvSQ7dZP8ys/xS+l8s+U4K6NOkZ5jva0xoIo7j3S4icGd61LL3/uGXl+yUy+tZSdFt3AK27rfQn30ej3BwcHO7oa4TpKfekl26iXZqZvkV7YURWHrsUTmbY7nj5gL2C6ubBDsy/Cu9bivVR28PcpmwjrJTr20lp02Dh0I1VEUhYyMDLmMXYXMZjP79u2zT7Qn1EXyUy/JTr0kO3WT/MpGVq6ZFTtPctcn//LI3O1sOGwtuG9rFMyiJzrw+7hbGdThpjIruEGyUzOtZSdnuoXTFLWkmHBdiqKQmZkpB0xUSvJTL8lOvSQ7dZP8bsz5lCwWbzvBku0nSUrPAaCKh4EH2oYy9JZwIoJ8y+29JTv10lp2UnQLIYQQQgghytT+hGTmbYpn7YGz5JqthVOdAG+G3hLOQ+3D8PeW2wzFNZJPQUai9e9mM1WMR+GsO9gmfa5SHQLCnNe/GyBFtxBCCCGEEOKGmcwWfj9kXfIr6sRle3uH8ECGdQmnd5OauJX3kl8aLtw0LfkUfNYWTNmAtUhtAbA1zzZunjBmlyrzk6JbOI2Xl5ezuyCug8FgoFGjRgWWmhPqIPmpl2SnXpKdukl+xTNm5LJ850kWbj3B6WTrkl/uBh39W9RmWJd6NA/1r5iOaLxw07SMRHtuhTJlW7dTYXZSdFdScXFxTJ06lY8++gg/P7/reo1//vmHDh06OCye09PTMRqN1K5d2+G+Op0ONzf58VMjnU5HQECAs7shrpPkp16SnXpJduom+RUu9kIa32yJ57tdp8nMtU54Vd3Hg0c71eWxjjcR7FfBJ1g0XrgJ9ZKqp5IyGo18/fXXzJw5k4MHD9KsWbNi95k5cybPPvssAKmpqfTv35933nmHMWPGFNh26dKlvPbaa1y4cMHha9lmL69SpQo6ne7GBiMqlMlkYs+ePbRu3VoOnKiQ5Kdekp16SXbqJvnlpygK/x69xLzN8fz130V7e6OQqgzvWo97WtbGy93FrwpI2HnlEnQFFECxXPm7cuW/ljx/L6StuOcd7nNlEuFS73OlnwXaittHufp+pR5jRX4uCmSnlkPQrkP+5aikbJdIeXl52c9Unzt3Dh8fH4fb33bbbXh6etofz549Gzc3N7p3786hQ4fIysqiQYMGVK1a1f663t7eDl/LYrEwadIk5syZQ2JiIhEREXz++ef07t27LIcoypFWlm+orCQ/9ZLs1EuyUzfJDzJzzHy/J4H5m48TeyENAJ0OejWuyfAu9egUEaieEym/vOjsHohKxmWK7gkTJnDw4EFWr15d4n0GDhxIUFAQn376aTn2rAzkndDBkQqc0MFkMpGWlkZOjnXJhszMTJKTk/H398fX15cFCxbQvXt3mjZtyl9//cXWrVt59dVXqVu3rr3oTkpK4r333iM3N5du3bphsVjIzMxkw4YNdO/eHbBeilXYP7xTpkzhww8/ZOTIkbRo0YJPPvmE//3vf8TGxhISElIhn4MQQgghhCjemeRMFm49wbIdJzFm5gLg6+nGQ+3CGHJLXepWd3zCpsIpCiRElWzbwPrgXgV0ADrQ6a1HENBZ/6vTX/27wzYqaB9bW0Xso8/zeZRmH9vYSrvPNf1MPgEb3ixN4qriEkV3dHQ0s2bNYs+ePSXe57fffuPPP//kyJEj5dizMnDNhA4OVeCEDrGxsTRu3Nj+2NfXl1deeYXk5GTmz5/P+PHjWbt2LU2bNuXSpUtMnjyZo0ePsnTpUtzdrUs7jBkzhrp167Jt27ZSX2ZlNBp5//33WbhwIXfddRc+Pj706NGDyMhIfv31V4YNG1am4xVCCCGEEKW3++Rl5m2K59foc5gt1suUbwqswtBbwnmwXShVvVxkyS+LBWLWwKaP4EwJa4kH5kHtVuXaLVFKZ/YCbzq5E+XH6UW3oiiMGjWKsWPHUr9+/RLtk5mZyejRo5k2bZrrT2zhYhM6NGzYkLS0NI4ePUrr1q3JyspCURSWLl3KqFGjWLJkCd27dyc2NpZ77rmH9evX079/f7Kysli6dClz5sxh5cqVLFu2jCNHjqDX68nJycHf35+MjAzAepb77Nmz5ObmEhMTA0BOTg7BwcEEBgby119/0aZNGywW630t1atXt28jXJ/BYKBFixYyi6tKSX7qJdmpl2SnbpUpv1yzhV8OnGXe5uPsO5Vsb+8cUZ3hXetxW6NgDHoXuYTcnAv7v4XNM+DSlZNwBk8wF/N7txBO4PSie86cOezdu5cRI0awZs0a7rzzTvsZ1cK8/fbbZGZm4ubmxp9//knPnj3L/x4SRYHcjNLvZ8os+XY56aV7bfcqVy/pKCG9Xo+Pj4/9DLXtknFfX1/ef/99HnzwQc6fP0+DBg2IjY2lW7du/P777/Z7vatWrcqECRMYPHgwJpMJg8GAwWBgyJAh7Nixw16I5+bmkpmZSadOnazDM5l46623GD9+PG3btkVRFPR66zqNv/76KwBdunQp3fiF03h4eDi7C+IGSH7qJdmpl2SnblrP73J6Dkt3nGTR1hOcS8kCwMNNz70trUt+Nal9fSvdlIucDNi9ELZ8CikJ1jZPf+g4EsK7wsJ7nds/cX2qVLde/Vvc1cFVqldcn8qQTlFs09pVvLS0NCIiIqhduzYDBgxg48aNZGZmsnHjxkLXcD558iQNGzakQ4cO3HHHHfzwww/cdNNNfP/99w4L7+zsbLKzr4aXkpJCWFgYiYmJ9qWy9Ho9er0ei8WCxWIhKyuLkydPUq9ePby9vVEUBXLS0b1bp3w+iOukvHoaPHzQ6XQ4irGo9gMHDtCiRQveeust7r77bpo3b46HhweKopCUlESNGjU4ceIEYWFh9tcxmUz5LicfNGgQ9evXZ8qUKQXeY/Hixbz++uvEx8c77Itt9nIPDw9atWpFWFgY69atK7TvWVlZxMfHExERgYeHh/0sOVzNz2w259unsHaDwYBOp8NkMuXrs+0I9rWTpRTW7ubmhqIo+dp1Oh0Gg8H+s1Rc+7U/e2oYk9lsZvfu3bRp0wZPT09NjKm4di2NyZZf27Zt7d8ltY/p2j5qdUy5ubn27567u7smxqTFnBy1Z2dn27MzGAyaGJMWcypsTNfmp4Ux2dqPnE9jwdYT/Lj3DNkm6/5Bvh482vEmBrUPJdi/iuuMKScVfdRclG1foLsyV5LiE4zSaTT69k9gdvdBST6FYVYHdEWc7VbcPGFMFGbf/MvaunJOefuolZ89h2MyJtjnwVIUhcOHD9GoUWP7iTqqVMcQWNelxpSWloa/vz9Go7HIZZideqb7+++/Jz09nT///JPAwEBeffVVmjdvzsKFCxk5cqTDfb755htq1qzJ+vXr8fT05Pnnn6du3bqsX7+eO+64o8D27777LpMnTy7QvmfPHvvZ26CgIOrXr098fDwXL1qXPvDy8iI3Nxdvb2+ysrIwZ2bgW4ZjLwvp6RkYLAa8vb3Jzc3Nd3m2m5sbXl5eZGdn5/tienh4cO7cOWbPng3A+vXrueuuu/D09MTDwyPfFzkiIsJeZOfm5jJ06FCmT5/OkSNH8PPzIzU1lfPnz7N7926ysrJo3Lgx1apVw2KxkJ2djaIopKeno9Pp8PHxwWw2k5WVZe+L2WzmrbfeIjY2lvnz55Oebj3TbzAUHFPeceTNCSA0NJTQ0FCOHDmC0Wi0t0dERBAcHEx0dDSZmVevOGjUqBEBAQHs2bMn3xezRYsWeHh4EBWVfxKOdu3akZOTw/79++1tBoOB9u3bYzQa7ZfQA3h7e9OyZUsuXbpEXFycvd3f35/GjRtz5swZEhIS7O2OfvZcfUyKopCcnExsbCxNmzbVxJi0mFNhY7Lld+LECRo0aKCJMWkxJ0djunDhAsnJyezevZuwsDBNjEmLOTkaU2xsrD07nU6niTFpMafCxnTo0KF8+al9TDVqBPHNbzv58XAKBy7m2p9rVsePHrUUOtZyx91wmfiYy1R1gTG5ZyVS68QPhCT8Arnp6IAs7xDO1HuQi3XuoF6DRgR7+RG9bx+ZmZl4dJ2LW46RevXC8fauwo4d2/H19eXKLGE0aNkRd9/aLp+TVr9PJR1T06ZNSffLZdcZU54Tq+do1y7UpcZU2Iniazn1TPfUqVPZsGEDf/75p73t4YcfJiIignfffdfhPiNHjsRkMjFv3jx7W8eOHRk+fDijRo0qsH2Znem+5vLyEp9dPncA3fw+xX4WyrB1ENK8dK995fLy0pzp3r17N+3bt6d69epcunTJfhQnJycHT09Pli1bxpNPPkl6ejq1atXio48+YuDAgVgsFkwmEydPnqR58+Z4enqSkZGBXq/Hy8uLrKwsYmNjqVPHejVASc50r1+/nr59+/LOO+/w8ssvF9l3OdPtOmOSM93qHpOc6VbvmORMt3rHJGe61T0mrZzpTs828cPesyzYeoL4S9YTHXod9G5Skye61qN9eGCJ+14hY0qMg82foNu37OqZ6+CmWG55HkuTe0HvVuhYbX00m83s3LnTnp3TxyTfpxKPSVEUoqKi8mXnimNSxZnusLCwfEc6AE6cOEHPnj2L3OfQoUP2xxaLhYSEBOrWretwe09Pz3zrS9u4ubkVmHnb9oG6ubmRd8krnW06e8/857oLu5s6X7u747WqC+zj7p3v9Uv02nnbC7m3+9r2Nm3a8PLLLzNgwAA6dOhgf95WPL3//vs89thjzJ49m379+vH5558zcOBA9Ho9Hh4eREZG2jMbOHAgkZGRDi8vz/fZOejLuXPnGDp0KHfccQcvv/yyw/5fu6/tsS2na+X9QpakvbCZ10vTrtPpHLYX1sfStrvqmGz/WBXVd7WN6Uba1TYmnU5n/7tWxlSSdrWPyfaLia1oK6rvahmTFnMqbEy27PK+j9rH5IhWx+QoP7WM6VRSBgu3Hmf5zlOkZlkLm6pebgzqcBODO9UlLLCKa43p/EHY9DH66O9AuVL4hHWEruOh4Z3odToKvkrh+TnKrsLHVEx7Zfs+laSPJpOp0OwcbQ/OG1NJOLXo7tu3L88++yxffvkl/fr14/vvv2fv3r306dOHlJQUvL29C0yq9tBDD9G2bVu+++47OnbsyKeffkp2drZMwlVCOp2OadOmER0dXeC5WbNmERsby4oVK5g9ezbDhg2je/fuLFq0iMGDBxf5unPnzqV+/fpFHjCxSU9Pp3///nh7e7No0aJCDxgI12QwGGjXrt0N/cMjnEfyUy/JTr0kO3VTY36KohB1wrrk128Hz3FlxS8iavgwtEs4A9qE4uPp9PmU8zu53brs15Grc/wQ2ctabNe9pdSTB4M6sxNWWsvOqd+2wMBA1q1bxwsvvMD48eMJCQlh+fLlhIeHEx4ezowZM7jvvvvy7XPzzTezYsUKJk6cSExMDPXr1+enn36iatWqzhlEcVQyE9+2bdt45ZVXmD59OkFBQYD1qoKJEycycuRILBYLQ4YMybdPTk4Op06dok+fPuzdu5eVK1eW6L3Gjh3L7t27mT17NnFxcfZL0GvUqEF4eHiZjkuUj5ycHLy9S3YVh3A9kp96SXbqJdmpm1ryyzaZWbv/LPM2xxN9OsXe3q1BDYZ3qUf3hkHoXWXJL7Devhn7h7XYPrH5SqMOmt4HXcdBrZY3/BZqyU4UpKXsnH6Iq1OnTmzevLlA+/Hjxwvdp2/fvvTt27cce1WGAsJgzC77THwOValeIWt055X3foQNGzbw8MMPc++99/LMM8+QnJxsf27SpElcvnyZoUOH8uuvv7Js2TL7fRc7d+4kISGBgQMHcvjwYapVq2bfz2w257sXIq9vv/0WRVEKTJY3ZMgQvvnmmzIdpyh7ZrOZ/fv3065du0IvYxKuS/JTL8lOvSQ7dVNDfpfSslmy7SSLt5/gYqr1RI+nm57724QyrEs4DWu62MkpixkO/QSbPoZzVybE0rtDy4HQZSzUiCyTt1FDdsIxrWWn/hGoQUBYhRfVRZkwYQJ//fUXgYGBWCwW5s6dS9euXe0Fr20CAtt/P/74Y7p06UKzZs3sl4IbDAZ69uxJq1atGD9+fIH3yMzMLHC/vo3RaLTPbO7j4yOXlwshhBBCXIdDZ1KYvzmen/aeIcdsPdlR08+TxzuHM6jDTQT6uNj64qZs2LccNn8CScesbe4+0HYodH4G/F1reV4hyooU3ZWQt7c3NWvWZPr06ej1epYsWYKiKPajSLZiOe/yXg888EC+19DpdCxcuLDQ93jqqad46qmnyqH3QgghhBCVl9mi8Mfh88zbHM+2uCR7e8uwAJ7oWo+7moXgbnA01ZgTZafBrm9g6+eQesba5hUAHZ+CjqOgSqAzeydEuZOiuxJ644038j2+doKC0NBQh0uQlTU5w61eWpnUorKS/NRLslMvyU7dXCG/1Kxcvo1KYMGW45xMsi5ja9DruKtZCMO71qPNTdWKeQUnyEiCHV/B9i8h87K1rWot6DzGenb7mpWByoMrZCeuj5ayc+o63c6QkpJS7FpqtjWh69WrV+IFz0X5kkyEEEIIURmdSEznmy3HWRmVQFq2dckvf293HuloXfKrdoALTjSVcsZ6VjtqPuRa1wQnMMJ6v3bLgdZJhIXQgJLUliBnuoWT2Bavt619KdRDURSMRiP+/v6SnQpJfuol2amXZKduzshPURS2xiUyb9Nx/og5j+0UWWSwL8O6hHN/61C8PVzwLGDiMdg8A/YuA0uutS2kuXXZryb3gr5i+yzfPfXSWnYudsOHa6lkFwFUuLz3jBdHsnAdZrOZmJgY+0R7Ql0kP/WS7NRLslO3iswvK9fMt1GnuOuTf3lkznY2HLYW3D1uDmLh8A6sH3crj3as63oF99n9sHIofNYOdi+0Ftx1u8Cj38Gof6HZ/RVecIN899RMa9nJmW4HbBOKmUwmJ/dE2Niy0MKSAUIIIYQQeV1IyWLxthMs2X6SxPQcALzdDTzQNpQht4QTGVz+9z6XmqLAiS3WNbZjN1xtb9jHusb2TZ2c1zchXIxUMA4YDAYMBgMpKSlUrepi6xpWUikpKfZchBBCCCG04ECCkfmb41m9/wy5ZutVfXUCvHm8c10Gtr8J/yruTu6hA4oCR36zFtuntlvbdHpoer+12A5p5tz+CeGCpOh2QKfTERwczNmzZ/H09JS1pMuBoijk5uaSlZVV5GdrW887JSWFWrVqSQ4uQKfT4e3tLVmolOSnXpKdekl26lbW+ZnMFtYfsi75tfP4ZXt7u7rVGN61Hnc0qYmbqy35BWA2waEf4d+P4MJBa5vBA1o9Cl2es06U5mLku6deWstOZi8vhKIonDt3DqPRKPcTO5lOp8Pf35+QkBDNfPGEEEIIUbkYM3NZsfMkC7ac4HRyJgDuBh39WtRmWJdwWoQGOLeDhcnNgn1LYfMncPm4tc3DF9oNh87PQNUQp3ZPCGeS2ctvkE6no1atWgQHB5Obm+vs7miOxWLh8uXLVKtWDb2+6KO57u7uclm5C7FYLFy6dIkaNWoUm51wPZKfekl26iXZqduN5nfsYhrfbD7Od7sTyMixTgoV6OPBox1v4rFOdanp56JLoWanQtQ869JfaeetbVWqQ8enocMI8HbBdcGvId899dJadlJ0F0PuIy4fJpOJU6dOUbNmTZkcTWUsFgtxcXEEBgZq4h/BykbyUy/JTr0kO3W7nvwURWFT7CXmbYpn438X7e2NQqoyvEs97mlVGy93F/39Mv0SbP8SdnwFWUZrm18o3PIstBkMHj7O7V8pyHdPvbSWnVQ7QgghhBBClIHMHDM/7DnN/M3xHL2QBoBOB7c3qsnwLuF0rl/ddW+VSz4FWz+DXQvAZL38neoNrJOjNX8Q3Dyc2z8hVEyKbiGEEEIIIW7AWWMmi7aeYOmOkyRnWG9L9PEw8GC7MIbeEk54DRc+O3zxCGyeAftXgOXKcrm1WkG38dCon1PW1xZCa6ToFk5hmxzNZY/2ikJJduom+amXZKdekp16mS0K2+OT2J9owByfRKf6QRj0V3Pcc/Iy8zYf59cDZzFZrBPvhgV6M/SWejzYLhQ/Lxdc8svm9G7rsl+H1wBXJg0O72YttiN6Wk/Rq5x899RLa9nJ7OVCCCGEEEJcY130WSavPsRZY5a9rZa/F6/1bYxFgfmb49lzMtn+XMd6gQzvWo9ejWvmK8xdiqJA/D/WYjvur6vtN/e1Ftuh7ZzWNSHUSGYvFy7NYrFw5swZateurYnJESoTyU7dJD/1kuzUS7JTn3XRZ3l68W6uPTN11pjFmKV77I89DHruaWVd8qtpbf+K7WRpWCxw5Ff490M4vcvapjNY79XuOhaCGzu1e+VFvnvqpbXspOgWTmGxWEhISCAkJEQTX6TKRLJTN8lPvSQ79ZLs1MVsUZi8+lCBgjsvvQ7G3BbJ4E7hBFX1rLC+lZo5F6K/g00fw8UYa5ubF7QebJ2NvFpd5/avnMl3T720lp0U3UIIIYQQQgCJadl8G3Uq3yXljlgU6BxRw3UL7txM2LMYNs8E40lrm6cftB8BnZ4G32Dn9k+ISkaKbiGEEEIIUekYM3I5cNrI/tPJ7D9l5MBpI6eTM0u8/4XUogtzp8gyws65sO0LSL+yPrhPEHQaDe2fAC8XvgReCA2Tols4hV6vJygoSBOXi1Q2kp26SX7qJdmpl2TnfGnZJqJPGzmQYGT/aSP7E5I5kZhRYDudDmr5eXGmmDPdAMFVvcqjq9cn7QJsmwU7v4bsFGub/03Q5Tlo/Ri4ezu3f04i3z310lp2Mnu5EEIIIYTQjMwcM4fOGtmfcLXIPnYxDUe/8d4UWIUWof60CPWneZ0AmtXxo4qHG13f+5NzxiyH93XrgBB/Lza9cpvzZym/fAK2zLReSm66cqAgqBF0HQfNBoDBhZcsE0IDZPZy4dIsFgvx8fHUq1dPM0ewKgvJTt0kP/WS7NRLsis/2SYz/51LZV+CkQMJyexPMHL0QhpmS8Fyuba/F81D/WkRGnClyPYnoIqHw9d9o38Tnl68Gx3kK7x1eZ53asF94bB1crQDq0AxW9vqtLMu+9XwLpCfM0C+e2qmteyk6BZOYbFYuHjxInXr1tXEF6kykezUTfJTL8lOvSS7spFrtnD0fBoHTidfKbKNxJxLIddcsMCu4etJy1B/mof60zI0gGZ1/Es16VmfZrX44rE2BdbpDvH34o3+TejTrFaZjKnUEqLg34/gv7VX2yJ6Wovt8G7W6+OFnXz31Etr2UnRLYQQQgghXIrZohB3Mc16ifhpI/sSkjl0JoVsk6XAtgFV3K1nr+tcLbJr+nmiu8ECtE+zWvRuEsLW2Its33+Yji0a0zkyqOLPcCsKxG20FtvH/73SqIPG/a2XkddpU7H9EUKUmhTdQgghhBDCaRRF4XhiBvsTku33YB88bSQ9x1xg26qebjS/cga7RR3rZeKh1bxvuMAujEGvo1NEIG5JnrSLCKzYgttigZjV1mL77F5rm94NWgyELs9DUMOK64sQ4oZI0S2cQq/XExoaqonLRSobyU7dJD/1kuzUS7K7SlEUTidnsj/hykRnp62FdkqWqcC23u4GmtXxy3cPdnh1H/QVfKa5wvMz5cCBb2HTDEg8am1z84a2Q+GWMeAfWjH90AD57qmX1rKT2cuFEEIIIUS5OJ+SdaXATrZfKp6UnlNgOw83PU1q+V2ZSdxaZNcP8nX+7OAVKScddi+ELZ9BSoK1zcsfOoyEjk+BTw3n9k8IUYDMXi5cmtls5siRIzRs2BCDweDs7ohSkOzUTfJTL8lOvSpLdolp2ey3rYV9pci+kJpdYDs3vY5GtarS/Mrl4S1C/WlYsyruBtc8o1Xu+WVehh1zYPuXkJFobfOtCZ2fgbbDwEtOEl2vyvLd0yKtZSdFt3AKRVEwGo1UsgstNEGyUzfJT70kO/XSYnbGjFwOnDay/8rl4fsTjJxOziywnV4HDYKrXl0LOzSARiFV8XJXzy/R5ZZf6jnY+hlEzYecNGtbtXDr/dotHwF3r7J9v0pIi9+9ykJr2UnRLYQQQgghCpWWbeLgaWthbT2TnczxxAyH20YE+dCiztVLxJvU9qOKh/y6mU9SHGyeCXuXgPnKpfY1m1lnIm9yHxjk8xJCa+RbLYQQQgghAMjMMXPobAoHrlwevv+0kWMX03B0summwCpXlujyp3mdAJrW8cPPy73iO60W56Jh08dw8HtQrix9FtbJusZ2gztkjW0hNEyKbuEUer2eiIgIzcxIWJlIduom+amXZKderppdtsnMf+dSrROcJVjXwj56IQ2zpWCFXdvfy7pMV56ZxAOqeDih1xXvhvM7uc267NfR3662Rfa2Ftt1bymbTgqHXPW7J4qntexk9nIhhBBCCI3LNVs4ej6NA6eT7ct1xZxLIddc8NfAGr6e1rPXtvuw6wQQVNXTCb1WMUWB2A3WYvvkFmubTm+9fLzrOKjVwqndE0KUDZm9XLg0s9lMdHQ0zZo108SMhJWJZKdukp96SXbqVdHZmS0K8ZfS2HfKukTX/oRkDp5JIdtkKbBtQBV3mtfxp2VogL3IDvHzQieXOtuVKj+LGQ79aL2M/NwBa5vBA1oOsk6QVr1+ufdXXCX/bqqX1rKTols4haIoZGZmamZGwspEslM3yU+9JDv1Ks/sFEXhRGIG+08b2X8qmf2njRw8bSQ9x1xg26qebjSr459vLezQat5SYBejRPmZsmHfMtj8iXWiNAB3H2g3zLr0l1/tiumsyEf+3VQvrWUnRbcQQgghhAooisLp5EzrEl1XzmAfSDCSkmUqsK23u4FmdfzyrYUdXt0HvV4K7BJJPnV1zWyzmSrGo3DWHWxn3KpUh4AwyE6DXd9Yl/5KPWt9zrsadHwKOoyEKoFO6b4QwrVI0S2EEEIIUU7MFoVtcUlsT8jGFJhE58ggDCUsfM+nZF2Z5CyZfQnWS8WT0nMKbOfhpqdxLb8rs4hbz2JHBvuW+H3ENZJPwWdtrWevsf6y3AJga55tDJ7QbjjsXw6Zl61tVWvDLWOgzRDw9K3gTgshXJlMpCacwrbgvb+/v1zWpjKSnbpJfuol2anPuuizTF59iLPGLHtbLX8v3ujfhD7NauXbNjEt+8oa2NZJzg6cTuZ8SnaB13TT67g5pGq+WcQb1qyKh5s2Zvh1CWf2wlfdS759YH3oOhZaPAxuMuGcK5F/N9VLLdmVtLaUolsIIYQQooytiz7L04t3c+0vWbZfHZ+/vQEe7np7kX06ObPAa+h10CC46tW1sEMDaBRSFS939U8q5NJKWnRXj4TbJkLje0AvmQhRGcns5cKlmUwm9uzZQ+vWrXFzkx9DNZHs1E3yUy/JTj3MFoXJqw8VKLgBe9uMP44WeC4iyIcWdazFdctQf5rU9qOKh2Rd4cy5JdtuwFyo3bp8+yJuiPy7qV5ay079IxCqZTYXnFlVqINkp26Sn3pJduqwIz4p3yXlhekUEUjPm4NpHupPszr++Hm5V0DvBGC9X/vyCUg6BonHrDOOJ135b/KpEr6I617yKq6SfzfVS0vZSdEthBBCCFGGLqQWX3ADDOpwE/e2qlPOvanEiiqsjQmgFFy3XAghyoMU3UIIIYQQZSi4askm0wqu6lXOPakEbqSw9vCFwAioXt/638Ar/zVlwaL7KmwIQgjtk6JbOIXBYKBFixYYDDLxiNpIduom+amXZKcOOSYL30YVfXmyDgjx96JDPVnDuUTKo7CuXh98gsDRrMhn9pbbUETFkn831Utr2UnRLZzGw8PD2V0Q10myUzfJT70kO9eWnJHDU4t3sS0uCb0OLIq1wM47oZqtxHujfxNZRzuvii6si1KlunXpL1PBJdvs3Dyt2wmXJ/9uqpeWspOiWziF2WwmKiqKdu3aaWJGwspEslM3yU+9JDvXdiIxnWHf7CTuYjq+nm58/mgbMnNMBdbpDilkne5KwZUK66IEhMGYXZCRaO222cyhQ4do0qQJbrazblWqW7cTLk3+3VQvrWWn/hEIIYQQQjjRrhNJPLlwF0npOdT292LesPY0CrGu19q7SQhbYy+yff9hOrZoTOfIIG2f4VZLYV2cgLCrRbXJRMbpXKjVEjTwy78QouLJvxxCCCGEENdp9b4zvLByHzkmC83r+PP1kHYE+12dIM2g19EpIhC3JE/aRQRqo+DWSmEthBAVRIpuIYQQQohSUhSFWX8d4/3f/gOgd5OafDKwFVU8NPKrlRTWQghRZnSKoijFb6YdKSkp+Pv7YzQa8fPzc3Z3Ki1FUTCbzRgMBnTyP19VkezUTfJTL8nOdeSaLbz2wwG+jUoA4Imu9fi/uxsXehbbZbOTwrpEXDY/USzJTr3Ukl1Ja0uNHI4VapSTk4O3t7ezuyGug2SnbpKfekl2zmfMzGX0kl1sjk1Er4M372nK453Di93PadlJYV0m5LunXpKdemkpOym6hVOYzWb279+vmRkJKxPJTt0kP/WS7JzvVFIGw77ZSeyFNHw8DHz2SBt6Ngp2vHHyKfvs12azmdjynP1aCutyJd899ZLs1Etr2al/BEIIIYQQ5WzPycs8uTCKS2k5hPh58fXQdjSt7e944+RT8Flb+zrPbkALgK15tnHztC5LVdLCWwprIYRQLZcpuidMmMDBgwdZvXp1iffJzc2lTZs2fPrpp/To0aP8OieEEEKISuvXA2cZu2Iv2SYLTWr5MW9oe0L8vQrfISPRXnAXypRt3S5v0S2FtRBCaJJLFN3R0dHMmjWLPXv2lGq/6dOnEx0dXU69EuXNYLvETqiOZKdukp96SXYVS1EUvvonjmnrYlAUuK1RMJ8Oao2PZxn9+rR/BexeKIW1Csh3T70kO/XSUnZOn71cURS6du3K7bffzltvvVXi/Y4ePUqHDh0ICAhg/vz5JT7TLbOXCyGEEKI4uWYLk346yLIdJwEY0rkuk/o3Ldk622f2wlfdr++NpbAWQgjVUM3s5XPmzGHv3r2MGDGCNWvWcOedd+Lu7l7sfqNGjWLChAn8+uuvFdBLUdYURcFoNOLv7+/SywCIgiQ7dZP81EuyqzgpWbk8s2Q3/x69hE4Hk/o1YViXemX/RhE9oU4bKaxdnHz31EuyUy+tZefUojstLY2JEyfSoEEDEhISWLRoEe+88w4bN27Ey6vwe6Xmz5+P0WjkhRdeKLbozs7OJjv76n1VKSkpAJhMJkwmEwB6vR69Xo/FYsFiuXpZl63dbDaT94KAwtpt68jZXjdvO1hn4StJu5ubm31tOhudTofBYCjQx8LaXX1MZrOZw4cP0759e3s/1T6movqupTHZsmvTpg2enp6aGFNx7Voaky2/tm3b4uHhoYkxXdtHrY4pNzfX/t1zd3fXxJhcMaczyZmMWLSbI+fT8HY38PFDLejVOBiTyVTyMWWllOgXLPNtk1BCWpT7mEB7OVXkmHJycuzfPYPBoIkxaTEnR2PK+zuLrc9qH5MWc3I0JkVRCmTnimMqKacW3d9//z3p6en8+eefBAYG8uqrr9K8eXMWLlzIyJEjHe5z8eJFXn31VdatW1ei6ePfffddJk+eXKB9z549+Pj4ABAUFET9+vWJj4/n4sWL9m1CQ0MJDQ3lyJEjGI1Ge3tERATBwcFER0eTmZlpb2/UqBEBAQHs2bMnX+AtWrTAw8ODqKiofH1o164dOTk57N+/395mMBho3749RqORmJgYe7u3tzctW7bk0qVLxMXF2dv9/f1p3LgxZ86cISEhwd7u6mNSFIXU1FQAzYwJtJeTozEpikJycjKxsbE0bdpUE2PSYk6FjcmW34kTJ2jQoIEmxqTFnByN6cKFCyQnJ7N7927CwsI0MSZXy+nYZRPvb0shOVshuKon799TH8/0k0RFnSzZmOLiMEd/T72Dn1ESx47FkpSQU65jstFSThU9pkOHDtm/ezqdThNj0mJOjsbk6+uL0Wi0Z6eFMWkxJ0djatq0KUC+7FxxTEWdKM7Lqfd0T506lQ0bNvDnn3/a2x5++GEiIiJ49913He7z6KOPEh4ezjvvvANAjx49ePPNNwu9p9vRme6wsDASExPt193L0SfnnOnevXu3nOlW4Zhs2cmZbnWOyZafnOlW35hyc3Pt3z050132Y1oXfZZx3+4jK9fCzTV9mT+sAyF+niUfU/JxlLUvojv2ByVlHrFRznSrYEzZ2dn2756c6VbXmMxmMzt37pQz3Sock6IoREVFufyZ7rS0NNe/pzssLCzfkQ6AEydO0LNnz0L3Wbp0KVWrVuXzzz8HrJeo9+vXj4kTJzJhwoQC23t6euLp6Vmg3c3NrcCZctsHeq28QZekvbAz8KVp1+l0DtsL62Np2509Jp1OR5UqVdDpdJoZU0n6qIUx2bKzbaOFMd1ou5rGZMvP1jctjKmk7Wofk5ubm/27Z9tG7WNyhZwURWHe5uNMWXsIRYHuDYP47JHWVPVyt/ezyL6bsuHfj+HfD9CZssDgAe2egKh5YC5i2TA3Twy+QeCgn5KTa40p73cv7zZqHpMWc3LUnvd3lmufV+uYCutjadtdfUxms7nQ7BxtD84bU0k49Ux3UlISERERTJs2jX79+vH999/z8ssvExMTQ2BgIN7e3gUmVTt+/Hi+xwMHDmTs2LH06dOHgICAYt9TZi8XQgghBIDJbOGtNYdYuPUEAI92vInJ9zTFzVDC+/Ti/oa1L0DiUevjiB5w94dQIxKST1nX4S5Mler51+gWQgihOqqYvTwwMJB169bxwgsvMH78eEJCQli+fDnh4eGEh4czY8YM7rvvvnz7hIeH53vs5eVFSEhIiQpu4TosFguXLl2iRo0aDo8kCdcl2amb5Kdekl3ZSss28ezS3Wz87yI6HfzfXY0Z0a1eyWbJTbsAv70GB761PvatCXdOhWYDrs48HhBmL6olO3WT/NRLslMvrWXn9CXDOnXqxObNmwu0X3tGuzB//fVX2XZIVAiLxUJcXByBgYGa+CJVJpKdukl+6iXZlZ2zxkyGfxPF4bMpeLnrmfFwa/o0Cyl+R4sZds2HDW9BthHQQYcn4baJ4OVf+G6SnapJfuol2amX1rJzetEthBBCCFFRok8beWLBTs6nZFPD15Ovh7SjZVhA8Tue3QdrxsHpXdbHtVpBv4+t62wLIYQQRZCiWwghhBCVwh+Hz/Pssj1k5JhpWNOXeUPbE1qtStE7ZaXAxqmwYzYoFvD0g9teh/ZPgP76J9URQghReUjRLZxCp9Ph7+9fsnvnhEuR7NRN8lMvye7GfLM5nrfWHMKiQLcGNfj80Tb4ebkXvoOiwKEfYd2rkHrW2tZsgPXe7aoluBQ9D8lO3SQ/9ZLs1Etr2Tl19nJnkNnLhRBCiMrDbFF4e80hvtlyHICB7cN4+75muBc1Q3lSHPzyEsRusD4OjIC+H0L928q/w0IIIVSjpLWl+u9KF6pksVhISEjIt/C8UAfJTt0kP/WS7EovPdvEqEVR9oL7lT6NePf+5oUX3KZs+Pt9mNXZWnAbPKD7BHh66w0V3JKdukl+6iXZqZfWspOiWziF1r5IlYlkp26Sn3pJdqVzPiWLh7/ayobDF/Bw0/P5I214ukf9wi9VjPsbvugCG6eAKcu65vbTW6Hnq+DudUN9kezUTfJTL8lOvbSWndzTLYQQQghNOXw2heHf7OSsMYvqPh589Xg72tat5njjkqy5LYQQQtwAKbqFEEIIoRl//XeBZ5bsJj3HTP0gH+YP7cBN1R3MUH6da24LIYQQpSVFt3AKvV5PUFCQJha7r2wkO3WT/NRLsiveom0nePPng5gtCp0jqvPlY23xr+JghvIKXnNbslM3yU+9JDv10lp2Mnu5EEIIIVTNbFF495fDzN0UD8ADbUOZ+r/meLhd88uarLkthBCiDMns5cKlWSwWjh07ppnJESoTyU7dJD/1kuwcy8wxM3rJLnvB/eIdDXn/gRb5C25FgYM/wOcdYPsX1oK72QAYsxM6jiz3gluyUzfJT70kO/XSWnZSdAunsFgsXLx4UTNfpMpEslM3yU+9JLuCLqRmMfCrrfx28DweBj2fDGzFmNsa5J+hPCkOljwAK4dC6lnrmtuDf4AH5kHVkArpp2SnbpKfekl26qW17OSebiGEEEKozn/nUhn+zU5OJ2dSrYo7Xz3ejvbhgVc3MGXD5pnw7wfWJcAMHtB1PHQdd8NLgAkhhBClIUW3EEIIIVTl36MXGb14N6nZJurV8GH+0PaE1/C5ukHc37D2BUg8an0c0QPu/hBqRDqlv0IIISo3KbqFU+j1ekJDQzUzI2FlItmpm+SnXpKd1bIdJ5n4YzRmi0KHeoHMfqwt1Xw8rE+66Jrbkp26SX7qJdmpl9ayk9nLhRBCCOHyLBaF6b/9x5d/HwPgf63rMG1AczzdDGCxXFlze7KsuS2EEKLCyOzlwqWZzWYOHz6M2Wx2dldEKUl26ib5qVdlzi4r18yYZbvtBffYXg346KGW1oL77D74uhesHW8tuGu1hCf/hLvfd5mCuzJnpwWSn3pJduqltezk8nLhFIqiYDQaqWQXWmiCZKdukp96VdbsLqVlM2JBFHtPJeNu0DH9gRb8r3UoZKda19ze/qV1CTCPqnD769B+hMutuV1Zs9MKyU+9JDv10lp2UnQLIYQQwiXFXkhl2Dc7OZWUib+3O7MHt6VTvUA4+COsm2BdAgyg6f3We7f9ajm1v0IIIYQjUnQLIYQQwuVsib3EqMW7SM0yUbd6FeYNbU99wwVYMhJiN1g3qlYP+n4Ikbc7t7NCCCFEEaToFk6h1+uJiIjQzIyElYlkp26Sn3pVpuy+jTrF/31/AJNFoV3danz1SHMC936h2jW3K1N2WiT5qZdkp15ay05mLxdCCCGES7BYFD5af4TPNsYC0L9lbT5sZ8Rj3Yuy5rYQQgiXI7OXC5dmNpvZt2+fZmYkrEwkO3WT/NRL69ll5Zp5fsVee8H9StdqzPSYhceSe60Ft08wDPgaBv+ouoJb69lpneSnXpKdemktO7m8XDiFoihkZmZqZkbCykSyUzfJT720nF1iWjYjF+1i14nLuOsVlreNoW30TMi6suZ2+xHWNbe9A5zd1eui5ewqA8lPvSQ79dJadlJ0CyGEEMJpjl1MY/g3OzmRmEF7r1PMq76Uqgf2WZ+s1RL6fQx12jq3k0IIIcQNkKJbCCGEEE6xPS6RkYt2YcpM4T2fH3nIshZdomuvuS2EEEKUlhTdwikMBgONGjXCYJBfptRGslM3yU+9tJbd97sTeOW7ffRStjOlymKqmxOtT2hwzW2tZVfZSH7qJdmpl9ayk9nLhRBCCFFhFEVhxoaj/PDnJt5y+4YehiuXksua20IIIVRGZi8XLs1kMrFz505MJpOzuyJKSbJTN8lPvbSQXbbJzEvLd2L+azq/e7xMD8M+FIMHdJ8Ao7dptuDWQnaVmeSnXpKdemktO7m8XDiNVpYAqIwkO3WT/NRLzdldTs9h5tx5PJ04g/ruZ62NET3QVZI1t9WcnZD81EyyUy8tZSdFtxBCCCHK1cmTx4lZ+DxvmP4CPeR41cCj73vQbADodM7unhBCCFGupOgWQgghRPmwWDj+++cEbnuXO0jHgg5j08ep1u8t1a65LYQQQpSWTKQmnMK24L23tzc6OcuhKpKdukl+6qW67M7u4/K3z1Dt8gEAYg31CRz4OYENOju5YxVPddmJfCQ/9ZLs1Est2ZW0tpQz3cJpPDw8nN0FcZ0kO3WT/NRLFdllp6L8+Q7K9tlUw0Kq4s3aoCe4d8QbeHupoP/lRBXZiUJJfuol2amXlrKT2cuFU5jNZqKiojQ1QUJlIdmpm+SnXi6fnaLAwR9RPmuPbvsX6LGw2tyJea1W8ODoKZW64Hb57ESRJD/1kuzUS2vZyZluIYQQQtyYpDj45SWI3YAOOG6pyRvm4fTuP4jnO9V1du+EEEIIp5KiWwghhBDXx5QNm2fCvx+AKYsc3JhluocF+v/x8eOd6HFzsLN7KIQQQjidFN1CCCGEKL34f2DNeEg8CsA2WvBq9hCy/OqxZEh7mtSWyUqFEEIIkNnLnd2dSktRFMxmMwaDwaVnJBQFSXbqJvmpl8tkl3YBfp8I+1cAkOVZg1czBvFDbiea1vZn3tD21PTzcl7/XJDLZCeui+SnXpKdeqklO5m9XLi8nJwcvL29nd0NcR0kO3WT/NTLqdlZLLBrPvwxGbKMKOiIrv0gj8b1JgUfejUO5pOBrfHxlF8tHJHvnbpJfuol2amXlrKT2cuFU5jNZvbv36+ZGQkrE8lO3SQ/9XJqdmf3wde9YO14a8Ed0pLP68+mf9x9pODDsC7hzB7cTgruQsj3Tt0kP/WS7NRLa9nJ/x2FEEII4Vh2KmycCtu/BMUCHlXJvPX/GHm4Ff8evIxeB5P6NWFol3rO7qkQQgjhsqToFkIIIUR+igKHfoJ1EyD1rLWt6f2c7vg6Q1ed4uiFy1TxMPDpoNbc3rimc/sqhBBCuDgpuoXTGAwGZ3dBXCfJTt0kP/WqkOzyrLkNQLV60PdD9nq2ZcSCKC6lZVPTz5Ovh7SnWR3/8u+PRsj3Tt0kP/WS7NRLS9nJ7OVCCCGEKLDmNgYP6DoOuo5j3X/JjF2xl6xcC41r+TFvaDtq+WtjchshhBDiepW0tpSJ1IRTKIpCcnIyleyYjyZIduom+alXuWYX/w980QU2TrEW3PW6w9NbUXq8yldbz/D0kt1k5VroeXMQK5/qLAV3Kcn3Tt0kP/WS7NRLa9lJ0S2cwmw2ExMTo5kZCSsTyU7dJD/1Kpfs0i7A9yNhQX9IPAo+wXD/XHj8J0zVInjtx2im/hKDosDgTnWZ83g7fGWG8lKT7526SX7qJdmpl9ayk/9zCiGEEJXNNWtugw7aPwG3vQ7eAaRm5TJm6R7+PnIRnQ4m9m3C8C7h6HQ6Z/dcCCGEUB0puoUQQojK5Ox+WDMOTkdZH9dqCf0+hjptATiTnMnwb3YScy4Vb3cDnwxsxR1NQ5zYYSGEEELdpOgWTqHT6fD29pazJiok2amb5KdeN5ydgzW3uf11aD8C9NYZYg8kGHliwU4upGYTVNWTr4e0o0VoQNkNopKS7526SX7qJdmpl9ayk9nLhRBCCC2zr7n9KqSesbY1vR/unAp+teybrT90nueW7SEz18zNNasyb1h76gTIhGlCCCFEYWT2cuHSLBYLFy5cwGKxOLsropQkO3WT/NTrurJLioclD8LKIdaCu1o9eOx7eHC+veBWFIV5m+IZuSiKzFwz3RrUYOXTnaXgLkPyvVM3yU+9JDv10lp2UnQLp7BYLMTFxWnmi1SZSHbqJvmpV6myM2XDP+/DrE4Qu9665nb3V2D0Voi8/epmZgtv/nyQt9YcQlFgUIebmDe0PX5e7uU4kspHvnfqJvmpl2SnXlrLzmWK7gkTJtC/f/8SbfvVV19Rq1Yt3N3dueOOOzh79mw5904IIYRQCdua23/mX3Obnv8H7lfPXqdnmxi5aBcLtp4A4P/ubsTU/zXD3eAyvxoIIYQQmuAS/2eNjo5m1qxZzJgxo9htN23axOuvv86iRYuIj48nKyuLF198sfw7KYQQQriyItbcpkZkvk3PGbN48Mut/BlzAU83PV882oaRt9bXzIQ1QgghhCtx+uzliqIwatQoxo4dS/369Yvd/r///uOLL76gV69eAAwbNoxp06aVdzdFGdPpdPj7+8sveCok2amb5KdehWZnscDub2DDmw7X3L7WwTNGnvgminMpWdTw9WDO4+1ofVO1ChhB5SXfO3WT/NRLslMvrWXn9NnLv/rqK8aNG8dnn31GUFAQd955J+7uJb+XbMKECRw4cIC1a9eWaHuZvVwIIYRqJJ+CjMTCn0+7CH9PK3TN7Wv9GXOeMUv3kJFjJjLYl/lD2xMWWKUcOi6EEEJoX0lrS6cW3WlpaURERFC7dm0GDBjAxo0byczMZOPGjXh5eRW7f2JiIpGRkSxevJi+ffs63CY7O5vs7Gz745SUFMLCwkhMTLR/MHq9Hr1ej8ViyXezvq3dbDaT92MqrN1gMKDT6TCZTPn6YDBY1z81m80landzc0NRlHztOp0Og8FQoI+Ftbv6mCwWC+fOnSM0NNT+WO1jKqrvWhqTLTvbvApaGFNx7Voaky2/2rVr4+bmpokxXdtHzYzJmIDhiw7oTNkURgF0gOLhi6Xna+jaP4nezd3hmBZtO8GbPx/EokDniEA+H9SKar5eFTsmNJhTCcaUm5vL2bNnCQkJsbepfUxazKmwMV2bnxbGpMWcHI1JURQSEhLs2WlhTFrMydGYdDodZ86coWbNmvbsXHFMaWlpJSq6nXp5+ffff096ejp//vkngYGBvPrqqzRv3pyFCxcycuTIYvcfPXo0t9xyS6EFN8C7777L5MmTC7Tv2bMHHx8fAIKCgqhfvz7x8fFcvHjRvk1oaCihoaEcOXIEo9Fob4+IiCA4OJjo6GgyMzPt7Y0aNSIgIIA9e/bkC7xFixZ4eHgQFRWVrw/t2rUjJyeH/fv329sMBgPt27fHaDQSExNjb/f29qZly5ZcunSJuLg4e7u/vz+NGzfmzJkzJCQk2NtdfUyKopCamkrt2rVJSkrSxJhAezk5GpOiKCQnJ5OamkrTpk01MSYt5lTYmGz5ZWdn06BBA02MSYs5AVQxHqVFEQU3WAvuy9XbENf8RXJ11YlIulxgTBZFYe1pT5ZEWScd7XGTJyOawZGD+ySnChzTyZMnOX36NDqdTjNj0mJOhY3p3Llz9vy0MiYt5nTtmHx9fTl06JA9Oy2MSYs5ORpT06ZNSUhIICEhId8l5q42ppKcKAYnn+meOnUqGzZs4M8//7S3Pfzww0RERPDuu+8Wue+8efN47bXX2Lt3LzVr1ix0OznT7ZpjMpvN7N69m/bt29v7qfYxFdV3LY3Jll2bNm3w9PTUxJiKa9fSmGz5tW3bFg8PD02M6do+amZMZ/fh9vVtFMf0xJ/Wy8odjCkjx8S4b/fzR4z1l4cXejfgqVvr2X+BkZwqZkzZ2dn2fzcNBoMmxqTFnAob07X5aWFMWszJ0ZjMZjM7d+60Z6eFMWkxJ0djUhSFqKiofNm54phUcaY7LCws35EOgBMnTtCzZ88i99uxYwdjx45l9erVRRbcAJ6ennh6ehZod3Nzw80t//BtH+i18gZdkvZrX/d62nU6ncP2wvpY2nZXGJPtlz4tjam4PmplTLZ/rIrqu9rGdCPtahuTTqez/10rYypJu+rGVEi/CryvwQDXvIfBYOBCShbDF+wk+nQKHm56PnywJf1b1i5x3wtrl5yub0y2fzfzvo/ax+SIVsfkKD+1j6mkfSxtu6uNyVF2hfW9sHZXG5MWc7q2LyaTqdDsHG0PzhtTSTh1ybC+ffty+PBhvvzySxISEpg5cyZ79+6lT58+pKSkkJubW2Cf8+fP079/f1555RXatm1LWloaaWlpTui9uBF6vZ6goCCHP9DCtUl26ib5VQ4x51K47/PNRJ9OIdDHg2VPdiy04BblT7536ib5qZdkp15ay87ps5dv27aNF154gT179hASEsJHH33EfffdR3h4ODNmzOC+++7Lt/2MGTMYN25cgdcp6TBk9nIhhBAuL/Uc/Pg0HPuz+G1H/g21W9kf/n3kIs8s2U1atomIIB/mD21P3eo+5ddXIYQQopJSxezlziBFt2uwWCzEx8dTr149zRzBqiwkO3WT/Fyc2QQ758KfUyAntWT75Cm6l2w/waSfDmK2KHSsF8jswW0JqOJRfv0VJSLfO3WT/NRLslMvtWRX0trSdUcgNM1isXDx4sV8kxQIdZDs1E3yc2EJUTCnJ6x7xVpwBzUu8a4Wi8LUXw7z2g/RmC0K97epw6InOkrB7SLke6dukp96SXbqpbXsnDqRmhBCCFHpZSTBH5Nh1wJAAS9/6PUm1L8dPm8PRS0b5uZJlns1xi7ZzbqD5wAY37shz94WmW+JFSGEEEI4jxTdQgghhDMoCuxdCutfh4xEa1vLR6D3W+AbZH08Zpf9OZPZzKFDh2jSpIl1xnLgkuLLEytOsS/BiIdBz/QHWnBf6zrOGI0QQgghCiFFt3AKvV5PaGioS9+jIRyT7NRN8nMR5w/B2vFwcqv1cVBj6PshhHfJv11AGASEYbYo7Ii7xH/uHqRlh9AxogbHLqYxbP5OTidnElDFna8Gt6NDvcCKH4solnzv1E3yUy/JTr20lp1MpCaEEEJUlOw0+Ps92DYLLCZwrwI9JkCn0WBwd7jLuuizTF59iLPGLHtboI8HGdkmskwWwqtXYf6wDtSrITOUCyGEEBVJJlITLs1sNnP48GHMZrOzuyJKSbJTN8nPSRQFDq+GzzvClpnWgrtRP3hmB3R5vsiC++nFu/MV3ABJ6TlkmSzUD/Lhh9FdpOB2cfK9UzfJT70kO/XSWnZyeblwCkVRMBqNJV5fXbgOyU7dJD8nSIqHX1+Go79bHwfUhbvfh4Z3Frmb2aIwefUhikoqPceMn7fjgl24DvneqZvkp16SnXppLTspuoUQQojyYMqGzTPh3w/AlAV6d+g6FrqOB48qxe6+Iz6pwBnua50zZrEjPonO9auXUaeFEEIIUdak6BZCCCHK2rGN8MuLkBhrfVyvu3WitBoNSvwSF1KLLrhLu50QQgghnEOKbuEUer2eiIgIzcxIWJlIduom+ZWz1HPw2/9B9HfWx7414c6p0GwAlHLd7OCqXmW6nXAe+d6pm+SnXpKdemktOym6hVPo9XqCg4Od3Q1xHSQ7dZP8yonZBDvnwp9TICcVdHroMBJ6/h94+Zf65bJyzfy4N6HIbXRAiL+XLBOmAvK9UzfJT70kO/XSWnbaOHQgVMdsNrNv3z7NzEhYmUh26ib5lYOEKJjTE9a9Yi2467SFJzfCXe9dV8F9/FI698/awoqdV4vua8+R2x6/0b8JBn3pzqCLiiffO3WT/NRLslMvrWUnZ7qFUyiKQmZmpmZmJKxMJDt1k/zKUEYS/DEZdi0AFGuB3etNaDME9IbreslfD5zlpVX7Scs2Ud3HgxkDW5GebSqwTneIvxdv9G9Cn2a1ymYsolzJ907dJD/1kuzUS2vZSdEthBBClIaiwN6lsP51yEi0trV8BHq/Bb5B1/WSOSYL7/56mPmbjwPQPrwanw5qQ4i/9X7t3k1C2Bp7ke37D9OxRWM6RwbJGW4hhBBCJaToFkIIIUrq/CFYOx5ObrU+DmpsnZU8vMt1v+Tp5EyeWbKbvaeSARjVPYIX77gZd8PVO8AMeh2dIgJxS/KkXUSgFNxCCCGEikjRLZzCYDDQqFEjDIbruwRTOI9kp26S33XKToO/p8HWWaCYwb0K9JgAnUaDwf26X/bPmPOM/3YfyRm5+Hm58eFDrejdpKbDbSU79ZLs1E3yUy/JTr20lp1O0cqF8iWUkpKCv78/RqMRPz8/Z3dHCCGEK1MUOLwa1k2AlNPWtkb9oM80CAi77pc1mS18tP4Is/46BkCLUH8+f6QNYYFVyqLXQgghhKgAJa0tZfZy4RQmk4mdO3diMpmc3RVRSpKdukl+pZAUD0sfgm8HWwvugLrwyLcwcMkNFdwXUrJ4dO52e8H9eOe6rHyqc7EFt2SnXpKdukl+6iXZqZfWspPLy4XTaGUJgMpIslM3ya8YpmzYPBP+/QBMWaB3h65joet48LixM9FbYi/x3PI9XErLwcfDwLQBLejfsnaJ95fs1EuyUzfJT70kO/XSUnZSdAshhBA2xzbCLy9CYqz1cb3u1onSajS4oZe1WBQ+2xjLjA1HsCjQKKQqnz/ahvpBvmXQaSGEEEK4Mim6hRBCiNRz8Nv/QfR31se+NeHOqdBsAOhubKbwpPQcxq7Yyz9HLgLwYNtQ3rq3Gd4e2pgcRgghhBBFk4nUhFPYFrz39vZGd4O/0IqKJdmpm+R3DbMJds6FP6dATiro9NBhJPT8P/Dyv+GX33UiiTFL93DWmIWXu5637m3GQ+2u735wyU69JDt1k/zUS7JTL7VkV9LaUs50C6fx8PBwdhfEdZLs1E3yuyIhCtaMg3P7rY/rtIW+H0HtVjf80oqi8PWmeKb9GoPJohBRw4dZj7WhUciNHeyV7NRLslM3yU+9JDv10lJ2Mnu5cAqz2UxUVJSmJkioLCQ7dZP8gIwkWP08zO1lLbi9AqDfDHhiQ5kU3MbMXEYt2sWUtYcxWRT6tajFz892veGCW7JTL8lO3SQ/9ZLs1Etr2cmZbiGEEJWDosDepbD+dchItLa1ehR6TQbfoDJ5iwMJRkYv3cWppEw8DHpe79eYxzrVdelL44QQQghRvqToFkIIoX3nD8Ha8XByq/VxUGPo9xHUvaVMXl5RFJZsP8lbqw+RY7YQWs2bWY+2oUVoQJm8vhBCCCHUS4puIYQQ2pWdBn9Pg62zQDGDexXoMQE6jQaDe5m8RXq2if/74QA/7T0DQK/GNfnwwZb4Vymb1xdCCCGEusns5cIpFEXBbDZjMBjkskuVkezUrdLkpyhweDWsmwApp61tjfpBn2kQcH2zhzvy37lURi/ZxbGL6Rj0Ol7pczNPdosol8+20mSnQZKdukl+6iXZqZdaspPZy4XLy8nJwdvb29ndENdBslM3zeeXFA+/vgxHf7c+DqgLd78PDe8s07f5blcCr/14gKxcCyF+Xnz2SGvahQeW6XtcS/PZaZhkp26Sn3pJduqlpexk9nLhFGazmf3792tmRsLKRLJTN03nZ8qGv9+HWZ2sBbfeHW59CUZvK9OCOyvXzCur9vPCyn1k5Vro1qAGa5/rWu4Ft6az0zjJTt0kP/WS7NRLa9nJmW4hhBDqd2wj/PIiJMZaH9frDn0/hBoNyvRt4i+l8/TiXcScS0Wng7G3N2TMbZEY9K576ZsQQgghnEuKbiGEEOqVeg5++z+I/s762Lcm3DkVmg2AMr4HbO3+s7zy3X7Ssk1U9/Hgk4Gt6dqgRpm+hxBCCCG0R4pu4TQGg8HZXRDXSbJTN03kZzbBzrnw5xTISQWdHjqMhJ7/B17+ZfpWOSYLU385zDdbjgPQPrwanw5qQ4i/V5m+T0loIrtKSrJTN8lPvSQ79dJSdjJ7uRBCCHVJiII14+DcfuvjOm2h70dQu1XZv9XlDJ5Zuod9p5IBeKp7fV68oyFuBpkSRQghhKjsZPZy4dIURcFoNOLv7+/SywCIgiQ7dVN1fhlJ8Mdk2LUAUMArAHq9CW2GgL7si+A/Dp9n/Lf7MGbm4u/tzocPtqRXk5pl/j4lpersKjnJTt0kP/WS7NRLa9ld928p+/bt46uvvmLixIm89dZbrFixgsTExLLsm9Aws9lMTEyMZmYkrEwkO3VTZX6KAnuWwGftYNc3gAKtHoUxUdBuWJkX3CazhWm/xvDEgiiMmbm0DPVnzbNdnVpwg0qzE4Bkp3aSn3pJduqltexK/ZvKzp07ueWWW+jTpw///PMPubm5nD17ls8++4zQ0FBGjRpFUlJSefRVCCFEZXP+EMy/C34aDRmJENQYhv0K980C36Cyf7uULB6Zs50v/z4GwNBbwvn2qc6EBVYp8/cSQgghROVQqsvLP/74Y959912mT5/OY489hptb/t1jYmJ44YUXaNWqFWvXrqV58+Zl2lkhhBCVRHYa/D0Nts4CxQzuPtBjAnR6Ggzu5fKWm45e4vnle0hMz8HX041pA5rTr0XtcnkvIYQQQlQeJS6633//fb766iu2bt1K/fr1HW7TqFEj1q5dy6RJk7j99tuL3FZUbjqdDm9vb03co1HZSHbq5vL5KQocXg3rJkDKaWtb4/7QZxr4h5bLW1osCp/+GcuMP46gKNAopCqzHm1DRJBvubzf9XL57EShJDt1k/zUS7JTL61lV6LZy5OSkujRowffffcdDRo0KNELP//88zRv3pwRI0bccCfLksxeLoQQLiopHn59GY7+bn0cUBfu/gAa3lFub5mYls3YFXv59+glAB5uF8bke5vi5a6dZUqEEEIIUT5KWlvKkmHCKSwWC5cuXaJGjRroy2HWYVF+JDt1c8n8TNmw+RP490MwZYHBA7qMhW7jwd273N426ngSY5bu4VxKFl7ueqbc15wH2pbP2fSy4JLZiRKR7NRN8lMvyU691JJdSWtL1x2B0DSLxUJcXBwWi8XZXRGlJNmpm8vld2wjfHELbHzHWnDX6w5Pb4HbXiu3gltRFOb8E8fDX23jXEoWEUE+/PRMV5cuuMEFsxMlJtmpm+SnXpKdemktO1mnWwghRMVLPQe//R9Ef2d97FsT7pwKzQZAOd6/ZczI5cVV+1h/6DwA/VvW5t37m+PrKf87FEIIIUT5uKEz3RaLha+++orDhw8DsHTpUnr27MmTTz5JcnJyWfRPCCGElphNsO1L+LSdteDW6aHjUzBmJzR/oFwL7gMJRvp99i/rD53Hw6Dn7fuaMXNgKym4hRBCCFGubug3jQkTJjB79mzWrVvHoUOHGDx4ML179+ann34iOzubhQsXllU/hcbodDr8/f01MyNhZSLZqZtT80uIgjXj4Nx+6+M6baHvR1C7Vbm+raIoLN52grfXHCbHbCEs0JtZj7Sleah/ub5vWZPvnnpJduom+amXZKdeWsvuhiZSq127NtOmTePxxx9n2rRp/Pbbb2zcuJGVK1cyevRoLl68WJZ9LRMykZoQQlSwjCT4YzLsWgAo4BUAvd6ENkOgnCdHScs28er3B1i97wwAvZvU5IMHWuJfpXzW+hZCCCFE5VEhE6mlpKTY1+HeuXMnvXv3BqBOnTrk5OTcyEsLjbNYLCQkJGhmcoTKRLJTtwrNT1FgzxL4rB3s+gZQoNWjMCYK2g0r94I75lwK93y2idX7zmDQ63jt7sZ8Nbitagtu+e6pl2SnbpKfekl26qW17G7oN55mzZrxySefsHLlSn7//Xe6desGwG+//UaTJk3KpINCm7T2RapMJDt1q7D8zh+C+XfBT6MhIxGCGsOwX+G+WeAbVL7vDayMOsV9n28m7mI6IX5erBjZiSdvjVD1ZWry3VMvyU7dJD/1kuzUS2vZ3dA93e+99x79+/dn1apV9O/fn27duvHaa6/x0Ucf8eOPP5ZRF4UQQqhGdhr8PQ22zgLFDO4+0GMCdHoaDOV/hjkzx8wbP0fzbVQCAN0a1GDGw62o7utZ7u8thBBCCOHIDRXd3bt358yZMyQmJlK3bl0ABg4cyJNPPkl4eHhZ9E8IIYQaKAocXg3rJkDKaWtb4/7QZxr4V8z613EX0xi9ZDcx51LR6WBcr4Y80zMSg169Z7eFEEIIoX43vE6Kr68vvr6+9sfNmze/0ZcUlYBerycoKAh9Od/TKcqeZKdu5ZJfUjz8+jIc/d36OKAu3P0BNLyj7N6jGGv2n+GVVftJzzFTw9eDTwa2pktkjQp7/4og3z31kuzUTfJTL8lOvbSWXYlnL1+wYAG//fYbU6ZMISIiorz7VW5k9nIhhCgjpmzY/An8+yGYssDgAV3GQrfx4O5dIV3INpmZuvYwC7aeAKBDvUA+HdSamn5eFfL+QgghhKi8ynz28iFDhjB27Fieeuopnn/+eRITE8uko6JyslgsHDt2TDOTI1Qmkp26lVl+xzbCrM6w8R1rwV2vOzy9BW57rcIK7lNJGTz05VZ7wf10j/osHdFRswW3fPfUS7JTN8lPvSQ79dJadqU6X9+hQwd+//137rjjDu655x6mTp1KZmZmefVNaJjFYuHixYua+SJVJpKdut1wfqnnYNVwWHQfJB0D35ow4Gt4/Ceo0aBM+1qUDYfO03fmv+xLMOLv7c68oe14pU8j3AzauAzNEfnuqZdkp26Sn3pJduqlteyu67eTvn37smnTJmrVqkWPHj2YO3cuJbxKXQghhBqZTbDtS/i0HUR/Bzo9dHwKxuyE5g9ABS3FlWu28O6vhxmxMIqULBMtwwJY+1xXbmtUs0LeXwghhBCitK77lIBOp2PYsGH8/fffJCYm0rVrV1avXn3dHZkwYQL9+/cv0bZ///03jRs3pkaNGnz00UfX/Z5CCCFKICEK5vSEda9ATirUaQcj/4K73gMv/wrrxjljFo/M2cbsv+MAGHpLOCtHdSa0WpUK64MQQgghRGnd8HV4Xl5evPLKK6xZs4a///6b3r17s23btlK9RnR0NLNmzWLGjBnFbnvx4kXuueceBg0axNatW1myZAkbN268zt4LZ9Hr9YSGhmpmRsLKRLJTt1Lll5EEq5+Hub3g3H7wCoB+M+CJ9VCrZXl3NZ9NRy/Rd+a/7Dx+GV9PN2Y92oY372mKh1vl+TmU7556SXbqJvmpl2SnXlrLrsSzl5fUiRMneP3118nKyuKDDz7gpptuKnJ7RVHo2rUrt99+O2+99Vaxrz9jxgy+/PJLDh8+jE6n46effmLlypUsXry4RP2T2cuFEKIYigJ7l8L61yHjyqSZrR6FXpPBN6hCu2K2KHz651E++eMoigKNa/kx69E21KvhU6H9EEIIIYS4Vklryxtep/tadevWZeHChezbtw8PD49it58zZw579+5lxIgRrFmzhjvvvBN3d/dCt9+3bx+33XYbuiv3D3bo0IFXX3210O2zs7PJzs62P05JSQHAZDJhMpkA65EUvV6PxWLJd7O+rd1sNue7Z72wdoPBgE6ns79u3nYAs9lconY3NzcURcnXrtPpMBgMBfpYWLurj8lisXD06FEaNWqETqfTxJiK6ruWxmTLrkGDBnh4eGhiTMW1a2lMtvwaNmyIu7t7wTFdikH/y4twcisASlAjzHd9gD68S4WPKTE9hxdXHeDfo5cAeKhtHSb1a4y3h/V/XVrOydGYTCaT/bvn5uamiTFpMSdH7Tk5OfbsbG1qH5MWcypsTNfmp4UxaTEnR2OyWCzExMTYs9PCmLSYk6MxARw5coTIyMh8Z7tdbUwldUNF95w5c5g5cybp6ekOn4+Liyty/7S0NCZOnEiDBg1ISEhg0aJFvPPOO2zcuBEvL8dLvqSkpNCkSRP7Yz8/P06fPl3oe7z77rtMnjy5QPuePXvw8bGeKQkKCqJ+/frEx8dz8eJF+zahoaGEhoZy5MgRjEajvT0iIoLg4GCio6Pzzd7eqFEjAgIC2LNnT77AW7RogYeHB1FRUfn60K5dO3Jycti/f7+9zWAw0L59e4xGIzExMfZ2b29vWrZsyaVLl/J9rv7+/jRu3JgzZ86QkJBgb3f1MSmKQmpqKjfffDOJiYmaGBNoLydHY1IUheTkZACaNm2qiTHZaCmnwsZky8/Dw4MGDRrYx6Q3ZRIau5haJ34AxYzFzZtT9R/lXN3/oVxwI8L3UoWOKSYxl5lRaSRlWvBy0zO8RRVuDcsiet+eSpGTozFduHCB5ORkUlJSCAsL08SYtJiTozEdPXqUkydPkpKSgk6n08SYtJhTYWM6ePAg586ds+enhTFpMSdHY/L19eXUqVP27LQwJi3m5GhMTZs2xWg0smvXLnt2rjimwmrWa93Q5eV+fn7cd999+c485zVkyJAi91+4cCFPP/00p06dIjAwEJPJRPPmzRk3bhwjR450uM/DDz9Mly5deO655wDrUQ4vLy9yc3Mdbu/oTHdYWBiJiYn2SwDk6FPFj8lsNrN7927at29v76fax1RU37U0Jlt2bdq0wdPTUxNjKq5d9WNKPYMl/ZL9uZiYwzRu3Bh3N+uZbuXMbvT/fogu9Yx1p8b9Mfd+B8WvToWPyWQyMXfTcT5YfxSzRaF+kA+fP9KayKCrl5NrNqdixpSbm2v/7rm7u2tiTFrMyVF7dna2PTuDwaCJMWkxp8LGdG1+WhiTFnNyNCaz2czOnTvt2WlhTFrMydGYFEUhKioqX3auOKa0tLTyv7y8ZcuWdO/enaFDh17X/gkJCXTs2JHAwEBrZ9zcaNGiBfHx8YXuExgYmO/IQ2pqapGXsXt6euLp6Vmg3c3NDTe3/MO3faDXyht0Sdqvfd3radfpdA7bC+tjadtdYUy2AzVaGlNxfdTKmGz/WBXVd7WN6UbaXXpMyafgs7boTdn2mTNbAGy9sn3enQPqwt0fQMM7cNzz8h1TSqaJF1buZcPhCwDc07I2797fHB9Px6+hqZxK0G77xcRWtBXVd7WMSYs5FTYmW3Z530ftY3JEq2NylJ/ax1TSPpa23dXG5Ci7wvpeWLurjUmLOV3bF5PJVGh2jrYH542pJG6o6J44cSL9+/dn5cqV1K5du8Dz8+bNK3L/sLCwfJcXgHUitp49exa6T/v27Vm2bJn98d69e6lTp06h2wvXpNfriYiIcPgDLVybZKcyGYlgyi5+u9aPw93Twd27/PvkwL5TyTyzdDcJlzPxMOiZ1L8Jj3a8yeFVVJWVfPfUS7JTN8lPvSQ79dJadjc0ikcffZRGjRoREhKCoigF/hSnb9++HD58mC+//JKEhARmzpzJ3r176dOnDykpKQ4vGb/nnnvYtGkTGzduxGQy8cEHH3DnnXfeyDCEE+j1eoKDgzXzRapMJDuNav+EUwpuRVFYuPU4D365lYTLmYQFevP96Ft4rFNdKbivId899ZLs1E3yUy/JTr20lt0NnekOCQnhvffe46677rqu/QMDA1m3bh0vvPAC48ePJyQkhOXLlxMeHk54eDgzZszgvvvuy7dPjRo1+PDDD7nzzjvx9/fHx8eHr7/++kaGIZzAbDYTHR1Ns2bNbuhSDVHxJDtRVtKyTUz4bj9r9p8F4I4mNXn/wZb4exe+gkVlJt899ZLs1E3yUy/JTr20lt0NFd333HMPTz75JM8++yy1atXK95xOp2Pw4MHFvkanTp3YvHlzgfbjx48Xus/o0aO54447OHz4MN27d5f1tlVIURQyMzNLdEWEcC2SnSgLh8+m8MyS3cRdSsdNr2PCXY14oms9ObtdBPnuqZdkp26Sn3pJduqltexuqOhetmwZ7u7ufPnllwWeK2nRfb0iIyOJjIwst9cXQgjVUxQ4sMrZvSjg26hTvP5jNNkmC7X8vfjskda0rRvo7G4JIYQQQpSLGyq6i5plXAghhBOlXYSfRsPR353dE7vMHDOv/xTNql3WNTFvbRjEjIdbEehT+AoUQgghhBBqd0NFtxDXy2Aw0KhRI03co1HZSHYqcOxP+OEpSDsPenewFJyUssK7dDGN0Yt389/5VPQ6GNerIc/0jESvl8vJS0q+e+ol2amb5Kdekp16aS27Ek0Hl5SURJs2bUp1Zvull15i/vz5190xoW06nY6AgAC5f1OFJDsXZsqB31+HRf+zFtxBjeGRFeDmWfR+bp5QpXq5dWv1vjPc8+km/jufSg1fTxY/0ZFnb28gBXcpyXdPvSQ7dZP81EuyUy+tZVeiM92BgYEMGDCAPn36sH79em666aYit3/nnXeYN28e27ZtK5NOCu0xmUzs2bOH1q1bO1zEXrguyc5FJR6DVcPh7F7r43ZPwJ3vWJcBG7PLul43YDKb+e+//7j55ptxsx09rlIdAsLKvEvZJjNT1hxm0bYTAHSsF8ing1oT7OdV5u9VGch3T70kO3WT/NRLslMvrWVX4hG89tpruLm50b59e2bMmMHDDz9cYN20uLg4xo8fz86dO1m/fj0NGjQo8w4L7TCbzc7ugrhOkp0LURTYuxR+eQly08G7GtzzGTTud3WbgLCrRbXJROrpXKjVEsrxf2KnkjIYvWQ3B04bAXimZ33G9WqIm0Eb6206i3z31EuyUzfJT70kO/XSUnal+o3rlVdeoWvXrowdO5aXXnqJO+64gzp16pCamsqBAwfYvHkzgwYNYs+ePQQHB5dXn4UQQgBkGWHNOIj+zvo4vBv8bzb413Fqt9YfOs8L3+4lJctEQBV3Pn6oFT0byf8ThBBCCFE5lfo0R5cuXdi5cyc7d+5k+/btnDx5El9fX4YMGcLSpUupWbNmefRTCCFEXqd2wnfDIfkk6Axw22vQZSzonTfhSK7Zwvu//cdX/8QB0CosgM8fbUOdAG+n9UkIIYQQwtl0ilZWHC+hlJQU/P39MRqN+Pn5Obs7lZZtwXtvb2/NTJBQWUh2TmYxw6aPYOO7oJghoC4M+BrC2pdo9/LK75wxizFLdxN14jIAw7vUY8JdjfBwk8vJy4p899RLsvv/9u47PKoyfeP4d0oqCQkloSb0roBURQFFQBRZAV1XxF11dxUb9rUsupa176oo2HCta1d0LfxgEbE3CARiBKRFIPQAmSSkzsz5/TFm1kASkpBk5j25P9fFdXFezpm8D3cm8Mw55z1mU37mUnbmMiW7mvaW5t+VLsaKjNSzeU2l7ELEsx3emwE/fxnYPva3MPFhiE6o1cvUd35fbtjLNW+sYv/BUuKj3Dx0Tn9OP7ZdvX4NCdB7z1zKzmzKz1zKzlx2yk6nICQkfD4faWlptlogoalQdiGy9iN4+sRAwx0ZB5OfhqnP1rrhrs/8fH6LRz9ezx+eX8b+g6X0adecD2eepIa7gei9Zy5lZzblZy5lZy67Zacz3SIi4ay0EBbPgrTnA9vtjwtcTt6qW0inlVNQwrVvrOKrjTkATBuWwh2T+hEdEbp7ykVERETCkZpuEZFwtfvHwLO3964LbJ94DZxyG7hDe7nVsqz9XPXaSvbklxAT4eLeKccwdVDHkM5JREREJFyp6RYRCTeWBcuehcW3ga8E4toEHgXW7ZSQTsvvt5j35Wb+8d+f8PktuifH8eT0QfRsEx/SeYmIiIiEM61eLiFhWRY+nw+XyxXWKxLK4ZRdAzu4D96/EtYvDGz3OA0mPwnNWtfLy9c1v9zCUm54azWfrNsDwOSB7bl3yrE0i9Jnt41F7z1zKTuzKT9zKTtzmZKdVi+XsFdaWkpMjJ7fayJl10A2fwbvzoCCXeCKgvF/h2GXQj3/Y1Pb/FZty+XKV1eyPbeISLeTOyf1Y9qwlLD+R9Cu9N4zl7Izm/Izl7Izl52y0+rlEhI+n4+MjAzbrEjYlCi7BuArg4/vgJcnBxru1r3gkqUwfEa9N9y1yc+yLF78OovfPv0N23OLSG0Zy7uXj+D84alquENA7z1zKTuzKT9zKTtz2S07nekWEQml/ZvhnT/BjpWB7cEXw2n3QWRsSKeVX1zGLfN/YMEPOwE4rV8b/vHbATSPjgjpvERERERMU6umu6ysjIiImv2Ha+HChfj9fiZOnFiniYmI2N7qN2DBDVBaANGJ8Js50Pc3oZ4Va3bkceVrK8nKOYjb6eDWM/rwxxM76+y2iIiISB3UuOlev349J598Mjt27OCpp54iKioKh8NBcXExl19+OQA//fQTV199NfPnz2fXrl1cf/31HDhwoMEmL2ZzufQ8X1Mpu6NUnBdotn94K7Dd6USYOg8SGuexW1XlZ1kWb6Vt42/v/0iJ10+7hGjmnj+IwZ1aNMq85Mj03jOXsjOb8jOXsjOXnbKr8erlW7du5ZRTTmHTpk04nU66du2Kw+EgKysLr9dLXl4ew4cPJykpiY8//pglS5Zw/vnn4/F4GrqGWtHq5SISUtlpMP9PcOBncLjg5Fth5PXgDO0/LIWlXm7/z4/MX5kNwMm9knjk3IG0bBbaZ4KLiIiIhKt6X73c5XJV+LQhIyOD2NhYYmMD9x1OmzYNp9PJhx9+SFRUlBpaqZZlWXg8HhISEnTJqmGUXR35ffD1bPj0PvB7ISEVzv4XpA5v1GlUlt/GPQVc8eoK1u8uwOmAG8b34vLR3XA6lW840XvPXMrObMrPXMrOXHbLrs6rl5cXb1kWb775JsuXL2fBggUkJCQAEBsba4u/IGkYPp+PdevW2WZFwqZE2dVB3g54+Sz45O5Aw91vKlz2ZaM33D6/xdcb9vLcktV8vWEvPr/F+6u285u5X7F+dwGt46J45c/DufKU7mq4w5Dee+ZSdmZTfuZSduayW3Y1OtP94Ycfsnr1ajweD08++SQA8+bNw+124/f72b9/P3/+85/56KOP8Pl8lJWVMWTIENxuLY4uIk3cuv+D96+Eov0Q0QzO+AcMPL/eHwV2JIsyd3LXh2vY6SkGYE7acmIjXRSWBv4xO75rSx6fdhzJ8dGNOi8RERERu6tRV7xq1So+//xzioqK+PjjjwH45JNPcLlc+Hw+brjhBsaPH8+PP/6Iz+fD6/XSt29fIiN1L6CINFFlRbD4dlj+bGC73QA4+3lo3b3Rp7IocyeXv7KSQxfwKG+4JxzThrnTBuF21fniJxERERGpQo2a7ttvv52LL76YMWPG8N577+F0OnnjjTeIjY0lJiaG6Oho/H4///73v4OXl3/zzTc0a9asQScv5nI4HMTExOgWBAMpuxrYvSawWNqeNYHtE66CU+8Ad+N/EOnzW9z14ZrDGu5fW73NozwNoPeeuZSd2ZSfuZSdueyWXY1PaxxacPm2w+Hgp59+4uDBgwwZMoRt27YBcODAAZKTk+txqmInLpeLAQMG2OpRAE2FsquGZcGyZ+HZUwINd7NkuGA+nHZvSBpugGVZ+4OXlFdlp6eYZVn7G2lGUld675lL2ZlN+ZlL2ZnLbtnVuOm2LItfP13syiuv5JJLLsHr9ZKUlMSiRYvo2bMn48aNw+PxsHbtWvr06dMgkxbz+f1+9uzZg9/vD/VUpJaUXRUK98Mb0+H/bgRvMXQfB5d/A93HhnRaGdm5NdpvT371jbmEnt575lJ2ZlN+5lJ25rJbdjVuuktKSigqKgLg4osvDj4u7OKLLwYgIiKCN954g7KyMt577z2++uorTjzxxIaZtRjP7/ezefNm27yRmhJlV4msL+CpEfDTAnBFwmn3w/lvQVxSyKa0cU8BV7+ezv0L19Vofy2gFv703jOXsjOb8jOXsjOX3bKr8fLiycnJPPHEEwA899xzle4THx/P22+/TXJyMjfddBOvvfZa/cxSRCQc+crgs/vhy0cAC1r1gHOeh3b9QzalrJyDzPlkA/9ZtR3/LxcnRbmdlHgr/0fLAbRNiGZYl5aNN0kRERGRJqTGTXd8fDyxsbFceeWVweb7mWeeYceOHURGRla453vx4sVMnTqV2NjY+p+xiEg42J8F8/8M29MC24P+ABMegMjQLCC5dV8hjy/dwHvp2/H90m2P7dOGa8f2IPtAIZe/shKgwoJq5T+175jUF5eeyy0iIiLSIGrcdE+ePJkPPviA3r17s3v3btq0acMrr7zCvn37aNOmTYV98/PzycjIoKysjIiIiHqftJjP4XCQkJBgmxUJmxJlB2S8BR9dD6X5EJ0Akx6HfpNDMpVt+wuZu3Qj81dm4/2l2R7TO5lrx/agf8dEAI7pkMBTFwyq8JxuCJzhvmNSXyYc0y4UU5da0nvPXMrObMrPXMrOXHbLzmH9enW0arz44ov079+fQYMGBcdGjBjBJZdcEryvu5zP52PUqFEkJCSwYMGCsPrLysvLIyEhAY/HQ/PmzUM9HRExSUk+/N9fYPXrge3UE2Dqs5CY0uhT2Z5bxBOfbuSt5duCzfaonklcN7YHx6W2qPQYn99iWdZ+9uQXkxwfuKRcZ7hFRERE6qamvWWNz3RfdNFFh42NGTOGzp07HzZeUlLC66+/zpIlS8Kq4Zbw4ff72bFjB+3bt8fprPF6fhIGmmx221cELiffvxkcThh9M4y8EVw1/jFaL3Z5inni0428uXwbpb7AfdondW/NdeN6MLhT9fdlu5wOhndp8Ut+LXCq4TZKk33v2YCyM5vyM5eyM5fdsjuq/y3ec889lY5feOGFfP7551x66aWUlpYSGRma59NK+PL7/WRnZ9O2bVtbvJGakiaXnd8P3zwOS/8Ofi8kpATObnc6oVGnsSevmCc/28Rry7ZS+suiaCd0bcV143rWahG0JpefjSg7cyk7syk/cyk7c9ktu3o/RWNZFtdddx1ZWVncf//9DBo0iKlTp9b3lxERaXj5u+C9GbD5s8B238kwaTbEVH75dkPYm1/C059v4pXvtgRXIB/WuSXXjuvBiG6tG20eIiIiIlI3NW66Fy5cSExMDNHR0cFPG3w+H16vl7KyMvLz89m+fTsrVqzg008/ZcuWLfz+979n0qRJDTZ5EZEG89MieP8KKNwHEbFw+oNw3O+hkW6Z2VdQwrwvNvPStz9TXBZotgd3asF1Y3tyYvdWunVHRERExBA1bronTpyI2+3GsiwcDgcOhwOXy4XL5cLtdtO8eXOSkpLo0aMHV111Fb/5zW/o3r17Q85dDOZ0OklKSrLF5SJNje2zKyuGj/8Gy54JbLc9Fs5+HpJ6NsqXP3CwlHlfbualb36msNQHwMCURK4b15NRPVofdbNt+/xsTNmZS9mZTfmZS9mZy27Z1Xj1cqfTybp16+jZ88j/8dywYQM9evQ46sk1BK1eLiJV2rMO5v8JdmcGto+/EsbeAe6oBv/SuYWl/OvLLF74OouDvzTbx3ZI4PpxPTm5V5LObIuIiIiEmZr2lrX66MDhcFBWVsbIkSN54oknKCkpqfDnCxYsYMCAAYwZMwav11u3mUuT4Pf72bRpE36/P9RTkVqyZXaWBWnPw7yTAw13sySY/g5MuK/BG25PURmPfryekQ9+ytxPN3Kw1Effds159g9D+OCqEzmld3K9Nty2zK+JUHbmUnZmU37mUnbmslt2tWq6LcviwIEDjBw5krlz55KSksI999zDsmXLOP300znnnHMYO3YsK1euxO1u3MfoiFn8fj979+61zRupKbFddoX74a3fw0fXgbcIup0Kl38DPcY16JfNLy7j8U82MPLBpTz2yQbyS7z0bhvP0xcMZsHVJzGub5sGObttu/yaEGVnLmVnNuVnLmVnLrtlV+vOODk5mfvuu4/77ruP7777jnPPPZc77riD5ORk1qxZQ5cuXRpiniIi9e/nr+DdSyFvOzgjYOydcPwV0ID3DxWUeHnpm5959svN5BaWAdAjOY7rxvVkQr+2ena2iIiIiM3U+XT0wYMHeeyxx8jLy+Oee+5h3rx5XHfddbz66qs0a9asPucoIlK/fF74/AH44p+ABa26w9nPQfuBDfYlC0u9vPztFp75fBMHfmm2uyY149qxPZl4bDtcarZFREREbKlOTXdWVhaTJk2iZcuWZGRkkJqayqWXXsrkyZM5+eSTWbJkCQkJCfU9V7ERp9NJx44dbbMiYVNifHYHtsD8P0P2ssD2wAsCjwOLimuQL1dU6uOV77bw9Oeb2HewFIAurZtxzak9mDSgfaM328bn14QpO3MpO7MpP3MpO3PZLbs6rV7+5z//mZKSEp5//nkiIiKC+xQWFnLaaafh9Xr59NNPiY6ObrCJ15VWLxdpwn54J3DvdkkeRCXApEfhmLMb5EsVl/l49futPPXZJnIKAotOpraM5ZpTe3DWwPa4Xfb4R0RERESkqappb1mrM93li/o8/fTTlS6UFhsby7vvvsszzzwTlg23hA+fz8f69evp2bMnLpcr1NORWjAyu5ICWHgTrHo1sJ0yHKY+Cy061fuXKi7z8ebybTzx6Ub25Aea7Y4tYrh6TA+mDOpARIibbSPzE0DZmUzZmU35mUvZmctu2dWq6R45ciQRERE4HA5cLhcRERHExMQQHx9P27Zt6dGjB0OGDGHGjBkNNV+xCcuy8Hg81PBCCwkjxmW3Ix3e+RPs3wQOJ4z6C4y6CVz1+4SFEq+Pt9KyefLTjez0FAPQITGGq8Z05+xBHYl0h8eZbePykyBlZy5lZzblZy5lZy67ZVfj/3W+//77REVFBa+r93q9lJWVkZ+fj8fjYfv27WzatInXX3+d7du3M3bsWG688UZOPfXUBpu8iEiV/H74di58cjf4y6B5h8DZ7c4n1uuXKfP5eWdFNnOXbmR7bhEAbZtHc+WY7pw7pCNRbvM/nRURERGRuqtx0z1p0qQav+jKlSt5/PHHmTBhAh988AGnn356nSYnIlIn+bvhP5fBpqWB7T6TYNLjENuy3r5Emc/Peyu3M+fTDWzbH2i2k+OjuPKU7vxuaArREWq2RURERKQWC6nVxfbt2+nQoUNDvXydaCG18OD3+8nJyaF169a2WZWwqQj77NYvhv9cDoU54I6B0x+AQReCo35WCff6/Ly/agePL93Aln2FALSOi+Lyk7sxfXhq2DfbYZ+fVEnZmUvZmU35mUvZmcuU7GraWzZo0x2O1HSL2JS3BD6+A75/KrDd5lg45zlI6lUvL+/zW3y4egePfbKBrJyDALRqFsllo7txwfGdiIkM72ZbREREROpXTXvL8P3YQGzN5/OxevVqfD5fqKcitRSW2e39CZ499X8N9/DL4M9L6qXh9vstPli9g/GPfs61b64iK+cgLWIjuOX03nx58ylcMqqrUQ13WOYnNaLszKXszKb8zKXszGW37Op3+V6RGrIsi6KiItusSNiUhFV2lgUrX4KFt4C3CGJbweSnoOdpR/3Sfr/Foh93MXvJetbvLgAgISaCS0d15cIRnYmLMvPHZ1jlJ7Wi7Myl7Mym/Myl7Mxlt+xCfqZ75syZOByO4K/u3bsf8Zh//OMftGnThubNm3P22Wezb9++RpipiISVogPw9oXw4TWBhrvryXD5N0fdcFuWxaLMXZzx+Jdc8epK1u8uID7azfXjevLVzadw5SndjW24RURERKTxhfx/jitWrGDBggWMGDEC4IgPP//iiy946aWX+OKLL3C5XFxzzTXccMMNvPjii40wWxEJC1u+hfl/hrxscLrh1L/BCTPhKBbasCyLJWv3MHvJen7ckQdAfJSbP57UhT+e1IWEmIj6mr2IiIiINCEhbbq9Xi+ZmZmMGjWKuLi4Gh2zbNkyzjjjDHr1CtyrOW3aNJ588smGnKY0AJfLRe/evY/4IYuEn5Bm5/PCF/+ALx4Cyw8tu8LZz0GHQXV+Scuy+OynvTy6ZD0Z2R4AmkW6uPjELvx5ZBcSYyPra/ZhQe89cyk7cyk7syk/cyk7c9ktu5A23RkZGViWxcCBA9m+fTujR49m3rx5pKamVnnMMcccw1VXXcWMGTOIj4/nueeeY9y4cY04a6kPDoeDxMTEUE9D6iBk2eVuhfmXwLbvAtsDzoczHoKo+Dq9nGVZfLEhh0c/Xs+qbbkAxEa6uHBEZy4Z2ZWWzezVbJfTe89cys5cys5sys9cys5cdssupE332rVr6devH3PmzKF169ZcffXVzJgxg4ULF1Z5zIQJE+jRo0fw3u+hQ4dyyy23VLl/SUkJJSUlwe28vMBlo16vF6/XC4DT6cTpdOL3+/H7/cF9y8d9Pl+Fm/irGne5XDgcjuDr/nocOGz1varG3W43lmVVGHc4HLhcrsPmWNV4uNdUviLhoEGDgvM0vabq5m6nmnw+H6tWrWLgwIFERUU1Sk2ONf/BueA6HCV5WFHNYeIj+PpOCez4S801rcmyLL7NOsBjn2xkxZYDAERHOLlgeCqXjOxCm4RY/H5/hb9LE3Oqarw8v+OOO47IyEhb1HToHO1aU1lZWfC9FxERYYua7JhTZeMlJSXB7Fwuly1qsmNOVdV0aH52qMmOOVVWk8/nY8WKFcHs7FCTHXOqrCbLskhPT2fAgAEVznaHW001FdKme/r06UyfPj24PXfuXLp27UpeXl6Vzzl766232LJlC+vWrSMpKYkbb7yRCy64gPnz51e6//33389dd9112Hh6ejrNmjUDICkpiW7dupGVlcXevXuD+3Ts2JGOHTuyfv16PB5PcLxr164kJyeTmZlJUVFRcLx3794kJiaSnp5eIfD+/fsTGRlJWlpahTkMGTKE0tJSMjIygmMul4uhQ4fi8XhYt25dcDwmJoYBAwaQk5PD5s2bg+MJCQn06dOHHTt2kJ2dHRwP95osyyI/Px/ANjWB/XKqrCbLssjNzWXjxo3069evYWuKjeDAv/9E0rbAB3H5CX1w/+5FIpK716mmH/eW8c66QtbuC/xgj3I5OLVzFL/pEUNidD77d2yhTYI9cip36PdeeX5btmyhR48etqjJjjlVVtOePXvIzc1l5cqVpKSk2KImO+ZUWU0bN25k//79rFy5EofDYYua7JhTVTWtWbOmQn52qMmOOVVWU1xcHAcOHAhmZ4ea7JhTZTX169cPn89XIbtwrCk6OpqacFhhtA67x+MhMTGRdevWBe/ZPtSUKVMYM2YMM2fOBP73QPIDBw5UeglCZWe6U1JS2LdvX7Cx16dPoTnTvXLlSoYOHaoz3YbVVJ7doEGDGvZM955MHPP/DPs2YOHAOvE6/KNuwhUZXeuavt+8j0c/Xs93WfsBiHQ7OX9YKpeN6kLruP9dRm6nnKoaL89v8ODBOtNtWE1lZWXB957OdJtVU0lJSTA7nek2r6ZD87NDTXbMqaoz3cuXLw9mZ4ea7JhTVWe609LSKmQXjjUVFBSQkJCAx+Op8qQxhPhM9/XXX8/xxx/PueeeC8Dy5ctxOp2kpKRUeYzX62X37t3B7Z07dwKH/8WXi4qKIioq6rBxt9uN212x/PK/0EP9OuiajB/6unUZdzgclY5XNcfajodDTeWfWtmppiPN0S41lf+wqm7uda7J74fvn4Ild4KvFOLb45g6D0eXkRWecViTmlZsOcCjH6/nq405AES4HJw3NJUrTulGu4SYSo9vkJqqmWNdx4/me8/hcAR/b5eaajJuek3l/zEpb9qqm7spNdkxp6pqKs/u11/H9JoqY9eaKsvP9JpqOsfajodbTZVlV9XcqxoPt5rsmNOhc/F6vVVmV9n+ELqaaiKkTffAgQOZNWsWbdu2xev1MnPmTC666CJiY2PJy8sjJiaGiIiKj+k58cQTeeSRR+jYsSMxMTHMnj2bE044gVatWoWoCqkLl8tF//79j+qbV0KjQbMr2AP/uRw2Lgls9z4TfjMHYlvW6mVWbcvl0Y/X8/n6wKVBbqeDc4emcOUp3emQWHWz3RTovWcuZWcuZWc25WcuZWcuu2UX0qb7D3/4A2vXruWss84iPj6eKVOmcN999wGBexNmz57N5MmTKxxz7bXXsmPHDv7+97+Tk5PDCSecwHPPPReC2cvRioy05+rQTUGDZLdxCbx3GRzcC+5oOO0+GPJH+NV9PEfyQ7aHR5esZ+m6PQC4nA7OGdSRq8Z0J6VlbP3P2VB675lL2ZlL2ZlN+ZlL2ZnLTtmF1T3djaH8HvAjXXcvDcvr9ZKWlsaQIUOqvBRGwlO9Z+ctgU/uhm/nBraT+8E5z0Fynxq/xI87PDz68QaWrA3ceuJ0wNRBHZk5pjudWjU7+jnaiN575lJ25lJ2ZlN+5lJ25jIlu5r2luFbgYjYX84GeOePsOuXFSiHXQrj7oaIml0Cvm5XHrM/3sCiH3cBgWZ78sAOzDy1B11aq9kWERERkdBT0y0ijc+yIP0VWHgTlBVCTEs46wnofUaNDl+/O5/HlmxgwQ+BhRQdDpjUvz1Xn9qD7slxDTlzEREREZFaUdMtIo2rKBc+uhZ+fC+w3WUUTHkGmrc/4qEb9xTw+Ccb+DBjB+U3xkzs345rT+1BjzbxDTZlEREREZG60j3dEhLlz9ErfwyHmOOostv6Hcy/BDxbwemGMbfBiGugkscy/FpWzkEe/2QD76/ajv+Xn1inH9OWa8b2oHdbvY9rQ+89cyk7cyk7syk/cyk7c5mSne7plrBXWlpKTEzTfnyTqWqdnd8HX/wTPn8ALD+06AxnPw8dB1d72JZ9B3n8k438Z9V2fL902+P6tuHasT3o1z7hKCpo2vTeM5eyM5eyM5vyM5eyM5edsqv+9JJIA/H5fGRkZODz+UI9FamlWmeXuw1ePBM+uy/QcPf/Hcz4stqGe9v+Qm5+J4MxD3/O/JXZ+PwWp/ZO5sOrTuLZPwxRw30U9N4zl7Izl7Izm/Izl7Izl92y05luEWk4a96HD2ZCsQci42DiIzDgd1Xuvj23iLlLN/J22ja8v5zZHt0zievG9WRgSmIjTVpEREREpP6o6RaR+ld6EBbdCitfCmx3GAxn/wtadq10952eIp78dBNvLN9KmS/QbI/s0Zprx/ZkcKcWjTVrEREREZF6p6ZbQsblcoV6ClJH1Wa364fAs7dz1gMOOOk6OOWv4Io4bNfdecU89dkmXlu2lVKvH4AR3Vpx3bieDO3csoFmL3rvmUvZmUvZmU35mUvZmctO2Wn1chGpH5YF3z8NH/8NfKUQ1xamzoOuow/bdU9+MU9/tplXv99CyS/N9rAuLblubE9O6NaqsWcuIiIiIlJrWr1cwpplWXg8HhISEsL6MQDyi9xtULgPAAuLgoIC4uLicFCenQM+vQc2LA5s9jwdznoCmlVsoPcVlPDMF5t5+dufKS4LNNuDO7Xg+nE9GdGtlb4XGoHee+ZSduZSdmZTfuZSduayW3ZquiUkfD4f69atY8iQIbjd+jYMa7nbYO5g8JYA4ADiq9rXFQWn3QtD/wy/+gG5/2Ap835ptgtLA6tQDkxJ5PpxPRnZo7UtfpiaQu89cyk7cyk7syk/cyk7c9ktO/MrEJGGVbgv2HBXK7EzTHsN2vQLDuUWlvKvL7N44essDv7SbPfvmMB143pycs8kNdsiIiIiYntqukWkfkydF2y4PUVlPPdVFi98lUV+iReAfu2bc93YnpzaJ1nNtoiIiIg0GWq6JSQcDgcxMTFqvuzEHUVecRkvfPUz//pqM/nFgWa7d9t4rhvXk/F92yjvMKD3nrmUnbmUndmUn7mUnbnslp1WLxeR6u1YBfMOX4H8UG8OeoX70qPwFJUB0LNNHNeN7clp/dridNrjB6aIiIiISLma9pbORpyTSJDf72fPnj34/f5QT0WOxJNdo91e/nYLnqIyuiU1Y86041h0zShOP7adGu4wo/eeuZSduZSd2ZSfuZSdueyWnZpuCQm/38/mzZtt80aypaJc+O8srLcurNHuHRKieey8gSy+bjSTBrRXsx2m9N4zl7Izl7Izm/Izl7Izl92y0z3dIlKRzwsrX4JP74XCfdS0db78lG4cN7BDg05NRERERMQ0OtMtIv+zaSk8MxIWXB94VFjrXqwYdB/FVkS1hxVbEez1xTXSJEVEREREzKEz3RISDoeDhIQE26xIaLycDbD4Nli/KLAd0wJO/isMuZjiLA9jvomjhSO/ysMPWPE83KZrI01Wjobee+ZSduZSdmZTfuZSduayW3ZavVykKSs6AJ8/BMvmgd8LTjcMvQRG3wSxLflpVz5/eXsVGdvzqnwJB9A2IZqvbh6DS/dxi4iIiEgTodXLJaz5/X6ys7NtsziCcXxeWPYsPH4cfPdkoOHucRpc/i2c/gClkYk8tmQDZ875kozteURHBH5UHNpSl2/fMamvGm5D6L1nLmVnLmVnNuVnLmVnLrtlp6ZbQsJubySjbFgCT58I/3dj4Ex3Uh+44F2Y/hYk9SQjO5ffzP2KR5esp8xnMbZPMp/deApPXzCItgnRFV6qbUI0T10wiAnHtAtRMVJbeu+ZS9mZS9mZTfmZS9mZy27Z6Z5ukaZi70/w31mw8ePAdkxLGDMLBl0ELjfFZT4eXbKeZ7/YjN+Cls0iuWNSX34zoD0Oh4MJCe0Y17ct327cy/cZaxnevw8ndE/SGW4RERERkWqo6Raxu8L98NkDsPxfYPkC920PvwxG/QViEgFY/vN+bn4ng805BwGYNKA9d07qS6u4qAov5XI6OL5rS9z7oxjStaUabhERERGRI1DTLSHhdDpJSkrC6dQdDg3GVwbLn4PP7ofi3MBYrzNg3N+hdXcADpZ4eWjROl7+bguWBcnxUdw75VjG9W1T5csqO7MpP3MpO3MpO7MpP3MpO3PZLTutXi5iN5YFGxYHLiXftyEwltwPJtwHXU8O7vblhr3cMv8HtucWAXDukI7MmtiXhJjqn8ktIiIiIiJavVzCnN/vZ9OmTbZZHCFs7FkLr0yF184NNNyxreHM2XDZl8GG21NYxl/eXs3vn1vG9twiOiTG8PIfh/HQOQNq1HArO7MpP3MpO3MpO7MpP3MpO3PZLTs13RISfr+fvXv32uaNFHIH98GCG+CpE2HTUnBGwIir4eqVMORicLoAWPzjLsY9+jlvr8gG4MITOrH4ulGM6plU4y+l7Mym/Myl7Myl7Mym/Myl7Mxlt+x0T7eIybylsPxZ+OxBKPEExvpMgnF3Q8uuwd32FZRwxwc/8lHGTgC6tm7Gg+f0Z2jnlqGYtYiIiIhIk6GmW8RElgXrFwXu296/KTDW5liYcD90Gfmr3Sw+WL2DOz/4kQOFZTgdcOmoblw7tgfREa4QTV5EREREpOlQ0y0h4XQ66dixo21WJGxUu3+ERbdC1ueB7WbJcOrtMHB68DJygF2eYma99wOfrNsDQO+28Tx0Tn/6d0w8qi+v7Mym/Myl7Myl7Mym/Myl7Mxlt+y0ermIKQr2wqf3wsqXwPKDKxJOuBJOuh6i//e9bFkWby7fxr0L1pJf4iXC5eCqU3pw+cndiHTb4weXiIiIiEioafVyCWs+n4+1a9fi8/lCPZXw5y2Brx+HOYNgxQuBhrvvWXDVchh7Z4WGe9v+Qn7/3DJuefcH8ku8DOiYwEczR3LN2B711nArO7MpP3MpO3MpO7MpP3MpO3PZLTtdXi4hYVkWHo+HJnahRe1YFqxbAItvgwNZgbF2A+C0+6HziRV29fstXvr2Zx5a9BNFZT6i3E5uGN+TP57YBberfj9bU3ZmU37mUnbmUnZmU37mUnbmslt2arpFwtGuHwL3bf/8ZWA7rg2cegcMmAaH3NuycU8BN8/PYMWWAwAM69KSB8/uT5fWzRp71iIiIiIicgg13SLhpGAPLL0HVr4MWOCKghEz4aTrICquwq5en595X25m9pINlHr9NIt0ccsZfZg+LBWn0xGa+YuIiIiISAVquiUknE4nXbt2tc2KhEetrBi+fwq+eBhK8wNj/abCuLsgMfWw3dfsyOOm+avJ3J4HwOieSdw39Vg6JMY0+FSVndmUn7mUnbmUndmUn7mUnbnslp1WLxcJJcuCtR/A4tshd0tgrP2gwPO2U48/bPcSr4+5Szfy1Geb8PotEmIiuP3Mvpw9qAMOh85ui4iIiIg0Fq1eLmHN5/OxevVq26xIWCc7VsGLE+GtPwQa7vh2MOUZ+PMnlTbc6VsPcObjXzFn6Ua8fosJ/dry8fWjOGdwx0ZtuJWd2ZSfuZSduZSd2ZSfuZSdueyWnS4vl5CwLIuioiLbrEhYK/m7YOnfIf1VwAJ3NJx4TeBX5OGLnxWV+nh48U88/3UWfgtax0Vy91nHcMax7Rp/7jTx7GxA+ZlL2ZlL2ZlN+ZlL2ZnLbtmp6RZpLGVF8O0T8NWjUFoQGDv2t4FVyRNTKj3k2037uOXdDLbsKwRgynEd+NuZfWnRLLKxZi0iIiIiIkdBTbdIQ7Ms+PE9+PgO8GwNjHUYAhMegJShlR6SX1zG/QvX8dr3gf3bJURz35RjOaV3cmPNWkRERERE6oGabgkJl8tF7969cblcoZ5Kw9q+MvC87W3fBbabd4Cxd8ExZx/2vO1yn67bw1/f+4GdnmIAzh+eyq2n9yY+OqKxZl2tJpOdTSk/cyk7cyk7syk/cyk7c9ktO61eLtIQ8nbCJ3fD6tcC2xGxcOK1gWduR8ZWesiBg6X8/aM1vJu+HYDUlrE8cPaxjOjWupEmLSIiIiIiNaXVyyWseb1eli9fjtfrDfVU6ldpIXz+EMwZ9L+Ge8A0mLkCTr65yoZ74Q87Gffo57ybvh2HA/50UhcWXTsyLBtu22bXRCg/cyk7cyk7syk/cyk7c9ktO11eLiFjl0cAAIH7tjPnB+7bzssOjKUMh9Puh46DqzxsT34xd7z/IwszdwHQPTmOB8/uz+BOLRpj1nVmq+yaIOVnLmVnLmVnNuVnLmVnLjtlp6Zb5GhlpwXu285eFthOSIFxd0G/qVDF87Mty+Ldldu5+6M1eIrKcDkdXD66GzNP7U6U2x73roiIiIiIiJpukbrzbIdP7oKMNwPbEc1g5HVwwlUQEVPlYTtyi/jrez/w2U97AejXvjkPndOffu0TGmPWIiIiIiLSiLSQmoRE+QPvY2JicFRxNjhslR6Erx+Hrx8Db1FgbOB0GHM7NG9X5WF+v8Vry7bywMJ1FJR4iXQ5uWZsDy4d1ZUIlznLKxidnSg/gyk7cyk7syk/cyk7c5mSXU17S53plpCJjIwM9RRqx++HH96GJXdC/o7AWOoJMOF+aH9ctYf+nHOQm+dn8H3WfgAGpSby0Dn96Z4c38CTbhjGZScVKD9zKTtzKTuzKT9zKTtz2Sk7c06via34fD7S0tLMWSBh2zJ4biy8d2mg4U5Mhd++BBcvrLbh9vktnv1iMxMe+4Lvs/YTE+Hib2f25e3LRhjbcBuXnVSg/Myl7Myl7Mym/Myl7Mxlt+xCfqZ75syZzJ07N7jdrVs3Nm7cWKNjzzvvPJKSkpgzZ05DTU+autxtgTPbme8EtiPjYOQNcPwVEBFd7aHrd+dz0zsZrNqWC8CIbq14YGp/UltV/tgwERERERGxn5A33StWrGDBggWMGDECAJerZis3//e//2Xp0qWsX7++IacnTVVJAXw9G76ZA95iwAHHXRC4bzu+TbWHlvn8PPXZJuYs3UCZzyI+ys2siX343dCUsL4nRURERERE6l9Im26v10tmZiajRo0iLi6uxscVFRVxxRVX8MADD5CYmNhwE5Smx++HjDdgyV1QEHh2Np1Oggn3QbsBRzz8h2wPf3lnNet25QNwau9k7plyDO0Sql7NXERERERE7Cukq5evXLmS0aNH06ZNG7Zv387o0aOZN28eqamp1R7317/+lRdffJEHHniAjh07csopp9T4DKJWLw8PlmXh8/lwuVzhc/Z3y7fw31thR3pgu0VnGH8P9D6zyudtlysu8/HYJxuY98VmfH6LFrER3PmbfvxmQPvwqa+ehGV2UmPKz1zKzlzKzmzKz1zKzlymZGfE6uVr166lX79+zJkzh9atW3P11VczY8YMFi5cWOUxW7du5ZFHHmHYsGFs3bqVxx57jNTUVN59991KAykpKaGkpCS4nZeXBwTOsnu9XgCcTidOpxO/34/f7w/uWz7u8/n49WcTVY2Xf1OUv+6vx4HDFgKoatztdge/0co5HA5cLtdhc6xqPNxrsiyL4uJi4uLiQl9T7lacS+/CueY/gblFxuEfeSPW0EvBHYUrMOEqa1q2OYdb3/uRzTkHATizfzv+dmYfWsZGBI8xNafK5l6eXUxMDG632xY1HWncTjWV5xcbG1ttrSbVdOgc7VqTz+ejuLiY6OhoXC6XLWqyY05V1VRUVER0dDQOh8M2Ndkxp6pq+nV+dqnJjjkdWhNQITs71GTHnCqryel0UlJSQmRkZIUeL9xqqqmQNt3Tp09n+vTpwe25c+fStWtX8vLyqvyk4MUXX6RNmzZ8/PHHREVFcc0119CpUyc+/vhjxo8ff9j+999/P3fddddh4+np6TRr1gyApKQkunXrRlZWFnv37g3u07FjRzp27Mj69evxeDzB8a5du5KcnExmZiZFRUXB8d69e5OYmEh6enqFwPv3709kZCRpaWkV5jBkyBBKS0vJyMgIjrlcLoYOHYrH42HdunXB8ZiYGAYMGEBOTg6bN28OjickJNCnTx927NhBdnZ2cDzca7Isi/z8fMaMGcP+/ftDUtPq5V/TdsNrtPt5Pk5/GZbDiTXw96Qnnk5ZRAtY9UO1NfXtfxz3fJDBGyt2YgGJUQ4uH9aSGRMHsWfPHtLWmJ8THP69Z1kWubm5pKam0q9fP1vUZMecqqqpPL/u3bvTo0cPW9Rkx5wqq2nPnj3k5uaSmJhISkqKLWqyY06V1fTTTz+xdetWEhMTcTgctqjJjjlVVdMPP/zArl27gvnZoSY75lRZTXFxcXz99dckJCQEGzfTa7JjTpXV1K9fPzIzMwEqNN3hVlN0dPULK5cL6eXlh/J4PCQmJrJu3Tp69epV6T6XXnopXq+X559/Pjg2fPhw/vjHPzJjxozD9q/sTHdKSgr79u0LNvb69Knxa/L5fKxcuZKhQ4cG59loNTkdOFa/jvXJ3TgKdgPg7zwSx4T7oc0xNarp6437mPX+j2QfCPzQOGdQB249vReJsZG2yqmyuZdnN2jQIKKiomxR05HG7VRTeX6DBw8mMjLSFjUdOke71lRWVhZ870VERNiiJjvmVNl4SUlJMLvyqxRMr8mOOVVV06H52aEmO+ZUWU0+n4/ly5cHs7NDTXbMqbKaLMsiLS2tQnbhWFNBQUH4X15+/fXXc/zxx3PuuecCsHz5cpxOJykpKVUek5KSwpo1a4Lbfr+f7OxsOnXqVOn+UVFRREVFHTbudrtxuyuWX/4XeqhfB12T8UNfty7jDoej0vGq5ljb8XCoqfxTq0at6eevYNGtsCsDB0DLrjD+Xpy9Tg/et11dTZ6iMu5bsJY307YB0CExhvunHsuonklHNfdwzqmyOZb/sKpu7qbVdDTjptVUfnlrdfubVlNNxk2vqfw/JuVNW3VzN6UmO+ZUVU3l2f3665heU2XsWlNl+ZleU03nWNvxcKupsuyqmntV4+FWkx1zOnQuXq+3yuwq2x9CV1NNhLTpHjhwILNmzaJt27Z4vV5mzpzJRRddRGxsLHl5ecTExBAREVHhmHPPPZfBgwczf/58hg8fzpw5cygpKeHEE08MURVSV0fzjVtr+7Pg47/B2g8C21EJMPomGHYpuCNr9BJL1uxm1n9+YHde4MqJP5zQiZsm9CYuKuRP3mt0jZqd1DvlZy5lZy5lZzblZy5lZy47ZRfyy8tvvfVWnn76aeLj45kyZQr33XcfzZo1o3PnzsyePZvJkycfdsyCBQu47bbbWLduHd26deOZZ56pcdOt1cubmOI8+PJh+O5J8JWCwwmDL4ZT/grNWtfoJfYVlHDXh2v4YPUOALq0bsaDZ/dnWJeWDTlzEREREREJYzXtLUPedDc2Nd3hwbIsPB5PhYUt6pXfB+mvwNK/w8FfFkLoegqcdh+06VvjOX6YsZM7P/iR/QdLcTrgklFduW5sT6Ij7PPJW201eHbSoJSfuZSduZSd2ZSfuZSduUzJrqa9Zc3XORepRz6fj3Xr1h22CEK9yPoCnhkNH14daLhbdYfz34Lfv1fjhnt3XjGXvLyCq19PZ//BUnq3jec/V57Iraf3adINNzRwdtLglJ+5lJ25lJ3ZlJ+5lJ257JZd07sZVexr36bAfdvrPgpsRyfAybfCkD/V+L5ty7J4Oy2bvy9YQ36xlwiXgytP6c4VJ3cn0q3PqEREREREpHbUdIv5ij3wxT/gu6fBXwYOFwz9U6Dhjq35fdfb9hfy1/d+4MsNOQAM6JjAQ+cMoFfb+IaauYiIiIiI2JyabgkJh8NBTEzM0d2j4fNC+suw9F4oDDTKdB8L4++F5N41fhm/3+Llb3/mof/+RGGpjyi3k+vH9eRPJ3XB7dLZ7UPVS3YSMsrPXMrOXMrObMrPXMrOXHbLTgupiZk2fQr/nQV7fgxst+4Fp90LPcbV7mX2FnDL/AyW/3wAgGGdW/LA2cfSNSmuvmcsIiIiIiI2UtPeUme6JST8fj85OTm0bt260ofPVylnIyy+DdYvDGzHtICT/wpDLgZXRPXH/orX5+fZL7N4dMl6Sr1+mkW6uOX03kwf3gmn0x6fqDWUOmcnYUH5mUvZmUvZmU35mUvZmctu2anplpDw+/1s3ryZli1b1uyNVHQAPv8HLHsG/F5wumHoJTD6plrdtw2wdmceN72TwQ/bPQCM7NGa+6ceS8cWsXUppcmpdXYSVpSfuZSduZSd2ZSfuZSdueyWnZpuCW8+L6x4AT69D4r2B8Z6nAbj74GknrV6qRKvjyeWbuTJzzbh9Vs0j3Zz+5l9OWdwR9vcLyIiIiIiIuFFTbeEr41LAvdt710X2E7qE7hvu/uptX6pVdtyuemd1azfXQDAaf3a8PezjiG5eXR9zlhERERERKQCNd0SEg6Hg4SEhMrPMO9dD4tnwYbFge2YljBmFgy6CFy1+5YtKvXxyMc/8dxXWfgtaNUskrvPOoYzjm2rs9t1VG12EvaUn7mUnbmUndmUn7mUnbnslp1WL5fGk7sNCvdV/edOF6S/Asv/9b/7todfBqP+AjGJtf5y323exy3zM/h5XyEAkwe252+T+tGyWWQdCxAREREREQnQ6uUSXnK3wdzB4C2p2f69zoBxf4fW3Wv9pQpKvDywcC2vfLcVgLbNo7lv6jGM6d2m1q8lh/P7/ezYsYP27dvbYmGLpkb5mUvZmUvZmU35mUvZmctu2ZlfgZihcF/NGu4WXeEP78O01+vUcH/20x7GP/J5sOGeNiyVxdePUsNdj/x+P9nZ2fj9/lBPRepA+ZlL2ZlL2ZlN+ZlL2ZnLbtnpTLeEl3Oegw6Dan1YbmEpf/9oLfNXZgOQ0jKGB6f2Z0T31vU9QxERERERkRpT0y3hxVH7iy8WZe7ktv/8SE5BCQ4HXDyiCzee1pPYSH17i4iIiIhIaKkrEWPtzS/hjg8y+b8fdgHQPTmOB8/uz+BOLUI8M3tzOp0kJSXZ4v6apkj5mUvZmUvZmU35mUvZmctu2anpFuNYlsV/Vm3nrg/XkFtYhsvp4PLR3bhqTHeiI1yhnp7tOZ1OunXrFuppSB0pP3MpO3MpO7MpP3MpO3PZLTt7fHQg4a9wf728zI7cIv744nKue3M1uYVl9G3XnPevPJEbT+ulhruR+P1+Nm3aZJuFLZoa5WcuZWcuZWc25WcuZWcuu2WnplsaXrEH/u8vR/USfr/Fq99vYfyjX/DpT3uJdDn5y2m9eP+qEzmmQ0I9TVRqwu/3s3fvXtv8EGxqlJ+5lJ25lJ3ZlJ+5lJ257JadLi+XhlVWDK+fD/s3HnlfdxTEtjpseMu+g9w8P4PvNgfOlg9KTeShc/rTPTm+vmcrIiIiIiJSr9R0S8Px+2D+n2DLVxDVHM5+HuKSAPD6fKxZs4a+ffvidv1yWXhsK0hMCR7u81u88HUW/1z8E8VlfmIiXNx4Wi8uGtEZl9MRiopERERERERqRU23NAzLgo+ug3UfgSsKpr0OnU8K/rHT76elow3O9u2hklUJN+zO56b5GaRvzQVgRLdWPDC1P6mtYhurAqmC0+mkY8eOtllNsqlRfuZSduZSdmZTfuZSduayW3YOy7KsUE+iMeXl5ZGQkIDH46F58+ahno59ffJ3+PKfgedun/sy9JlUo8PKfH6e/mwTc5ZupNTnJy7KzayJfThvaAoOh85ui4iIiIhIeKhpb2mPjw4kvHz3dKDhBjhz9mENt89v8fWGPTy1YDlfb9iDzx/43Cdzu4ez5n7Nwx+vp9TnZ0zvZD6+fhTThqWq4Q4jPp+PtWvX4vP5Qj0VqQPlZy5lZy5lZzblZy5lZy67ZafLy6V+/fAOLLo58Psxt8HgCyv88aLMndz14Rp2eooDA1/uoW3zKAakJLJkbaABT4yN4M5J/ThrYHs122HIsiw8Hg9N7CIZ21B+5lJ25lJ2ZlN+5lJ25rJbdmq6pf5sXALvzQj8fvhlMPLGCn+8KHMnl7+ykkPfOrvyStj1424AJh7bjjt/04+k+KhGmLCIiIiIiEjDUtMt9SN7Bbz5B/B74Zhz4LT74VdnqX1+i7s+XHNYw/1rLWIjeHzacVqZXEREREREbEP3dMvR27seXj0Hyg5CtzEw+anDViRflrX/f5eUV+FAYRnLsvY35EylHjidTrp27Wqb1SSbGuVnLmVnLmVnNuVnLmVnLrtlpzPdcnQ82+GVqVC0HzoMhnP/De7Iw3bbk199w13b/SR0nE4nycnJoZ6G1JHyM5eyM5eyM5vyM5eyM5fdsrPHRwcSGoX7Aw23Zxu06gHnvw1RcZXumhwfXaOXrOl+Ejo+n4/Vq1fbZjXJpkb5mUvZmUvZmU35mUvZmctu2anplropLYTXfgd710F8e/j9u9CsVZW7D+vSklZxh58BL+cA2iVEM6xLywaYrNQny7IoKiqyzWqSTY3yM5eyM5eyM5vyM5eyM5fdslPTLbXnK4O3L4TsZRCdGGi4E1OrPaSw1FvlN1v5sml3TOqrRdRERERERMRW1HRL7fj98MFM2LAY3DFw/luQ3KfaQyzL4rb/ZLK3oJSWsZG0aV7xcWBtE6J56oJBTDimXUPOXEREREREpNFpITWpnSV/g9Wvg8MF574EqcOPeMg7K7J5f9UOXE4Hz144hIEpiSzL2sfPuw/QuU0LhnVppTPcBnG5XPTu3RuXyxXqqUgdKD9zKTtzKTuzKT9zKTtz2S07h2WXC+VrKC8vj4SEBDweD82bNw/1dMzy9WPw8d8Cv5/8NAycdsRDNu4pYNKcrygq8/GX03px5SndG3iSIiIiIiIiDa+mvaUuL5eaSX/1fw33+Htq1HAXl/m46rWVFJX5OKl7ay4f3S34Z16vl+XLl+P1ehtqxtJAlJ3ZlJ+5lJ25lJ3ZlJ+5lJ257Jadmm45sp8WBu7jBhhxNYyYWaPD7vu/tazblU/ruEge+d0AnIdcQm6XRwA0RcrObMrPXMrOXMrObMrPXMrOXHbKTk23VG/Lt/D2RWD5YOB0GHd3jQ5blLmLl7/dAsDD5w7U87dFRERERKRJUtMtVdv9I7z+O/AWQ88JMOlxcBx5wbPtuUXcPD8DgBmjujK6Z1JDz1RERERERCQsaSE1qVzuVnhuPOTvhJTj4ffvQWTsEQ/z+vycN+870rYcYEBKIm/POIFI9+Gf7ZQ/8D4mJgZHDRp5CR/KzmzKz1zKzlzKzmzKz1zKzlymZKeF1KTuDubAv6cEGu7kvnD+GzVquAEe+2QDaVsOEB/lZs55x1XacJeLjIysrxlLI1N2ZlN+5lJ25lJ2ZlN+5lJ25rJTdmq6paKSfHj1HNi3ERJS4YL5ENOiRod+szGHuZ9uBOC+qceS2qrqRt3n85GWlmarBRKaCmVnNuVnLmVnLmVnNuVnLmVnLrtlp6Zb/sdbAm9eADvSIbYV/P5daN6+RofuKyjh2jdXYVlw3tAUJg2o2XEiIiIiIiJ2pqZbAvx+eO8y2PwZRDSD6W9D6x41PNTihrdXsye/hO7JcdwxqV/DzlVERERERMQQaroFLAsW3Qw/vgvOCDjvFegwuMaHP/91Fp/9tJcot5O55x9HTKSrAScrIiIiIiJiDq1eLvD5Q/DpvYADznkOjjm7xoeu3pbLOU9/Q5nP4t4pxzB9eKcaHWdZFj6fD5fLFdYrEsrhlJ3ZlJ+5lJ25lJ3ZlJ+5lJ25TMlOq5dLzaQ9/0vDDZz+UK0a7vziMma+nk6Zz+L0Y9py/rDUWn3p0tLSWu0v4UPZmU35mUvZmUvZmU35mUvZmctO2anpbsrWvA8Lbgj8ftRNMPzSGh9qWRaz3stk6/5COiTG8MDU/rX6FMrn85GRkWGbFQmbEmVnNuVnLmVnLmVnNuVnLmVnLrtlp6a7qcr6Aub/GSw/DL4ITvlrrQ5/e0U2H6zegcvp4PFpx5EQG9Ew8xQRERERETGYmu6maOdqeP188JVCn0kw8RGoxVnqjXvyueP9HwG4YXxPBneq2XO8RUREREREmho13U3Nvk3wytlQmg+dR8LUf4Gz5quNF5f5uOq1dIrKfJzUvTWXjepW56m4XFrl3FTKzmzKz1zKzlzKzmzKz1zKzlx2yk6rlzcl+bvh+fFw4Gdoeyxc9H8QXbu/g7+9n8nL326hdVwk/3fNSJLjoxtmriIiIiIiImFMq5dLRcWewBnuAz9Diy5wwbu1brgXZe7i5W+3APDwuQOPquG2LIvc3Fya2Gc+tqDszKb8zKXszKXszKb8zKXszGW37NR0NwVlxYF7uHf/AM2S4ffvQVxyrV4i+0AhN72zGoAZo7syumfSUU3J5/Oxbt0626xI2JQoO7MpP3MpO3MpO7MpP3MpO3PZLbuQN90zZ87E4XAEf3Xv3r3Gx5aVlXHsscfy2WefNdwETefzwvw/wZavIKo5XDAfWnap1Ut4fX6ueWMVecVeBqQkcuP4Xg00WREREREREXtxh3oCK1asYMGCBYwYMQKo3Q3zDz30EJmZmQ01NfNZFiy4DtZ9BK4omPY6tOtf65eZvWQDK7YcID7KzZzzjiPCFfLPakRERERERIwQ0qbb6/WSmZnJqFGjiIuLq9WxGzZs4J///CedO3dumMnZwdJ7YOXL4HDCOc9B55Nq/RLfbMzhic82AnDf1GNJbRVbL1NzOBzExMTgqMWjyiQ8KDuzKT9zKTtzKTuzKT9zKTtz2S27kJ6yzMjIwLIsBg4cSExMDBMmTGDr1q01OnbGjBnccsstdOrUqYFnaajvnoYv/xn4/ZmPBp7HXUs5BSVc8+YqLAumDUth0oD29TY9l8vFgAEDbPUogKZC2ZlN+ZlL2ZlL2ZlN+ZlL2ZnLbtmF9Ez32rVr6devH3PmzKF169ZcffXVzJgxg4ULF1Z73AsvvIDH4+GGG2444r4lJSWUlJQEt/Py8oDAWXav1wuA0+nE6XTi9/vx+/3BfcvHfT5fhZXzqhp3uVw4HI7g6/56HDhsIYCqxt1uN5ZlVRh3OBy4XK7D5ljZuCNzPq5FNwPgP2UW/gEXwCG1Hqkmv9/ihrdWsTe/hB7Jcfx1Qq8KdR1tTX6/n/3795OcnBzcPlKtdsvJ1Jr8fj/79u2jdevWRERE2KKmI43bqaby/JKSknC73bao6dA52rUmr9fLvn37aNWqFW632xY12TGnysbLysrIycmhVatWwTHTa7JjTlXVdGh+dqjJjjlVVpNlWezevTuYnR1qsmNOldXkcDjIycmhRYsWwezCsaaaCmnTPX36dKZPnx7cnjt3Ll27diUvL6/K55zt3buXW2+9lUWLFuF2H3n6999/P3fddddh4+np6TRr1gyApKQkunXrRlZWFnv37g3u07FjRzp27Mj69evxeDzB8a5du5KcnExmZiZFRUXB8d69e5OYmEh6enqFwPv3709kZCRpaWkV5jBkyBBKS0vJyMgIjrlcLoYOHYrH42HdunXB8ZiYGAYMGEBOTg6bN28OjickJNCnTx927NhBdnY2CXvT6LXyb4E/HH4ZWR2msvdXX7emNX20oYjP1xcS5XYy9/xBrPsxo15rsiyL/Px8xowZw/79+6utqZydcjK5pvJHOKSmptKvXz9b1GTHnKqqqTy/7t2706NHD1vUZMecKqtpz5495ObmkpiYSEpKii1qsmNOVdW0detWEhMTcTgctqnJjjlVVdOuXbuC+dmlJjvmdGhNcXFxrF69moSEhOBlyqbXZMecKqupX79+wXn8+hLzcKspOrpmj1B2WGH08DOPx0NiYiLr1q2jV6/KV8iePn06nTt35t577wXg5JNP5s477+Tkk0+udP/KznSnpKSwb9++YGNvm0+fti3H9coUHGUHsY45G8fUf+GHWte0elsuv3v2e8p8FvdOPobpx3eq95p8Ph8rV65k6NChwb/7I9Vqm5wMr6k8u0GDBhEVFWWLmo40bqeayvMbPHgwkZGRtqjp0DnataaysrLgey8iIsIWNdkxp8rGS0pKgtm5XC5b1GTHnKqq6dD87FCTHXOqrCafz8fy5cuD2dmhJjvmVFlNlmWRlpZWIbtwrKmgoICEhAQ8Hk+VJ40hxGe6r7/+eo4//njOPfdcAJYvX47T6SQlJaXKY1577TXi4+N54oknACgoKODMM8/ktttu45Zbbjls/6ioKKKiog4bd7vdh50pL/8LPdSvg67JeFVn4Gsz7nA4Kh2vao7OfRtxvnEelB2EbmNwTH4anE6cVH7pQ1VzLyzzc+1bGZT5LM44ti3nD09tsJrKP7WqsqZajhuRk01qKv9hVd3cTavpaMZNq8nhcAR/b5eaajJuek3l/zEpb9qqm7spNdkxp6pqKs/u11/H9JoqY9eaKsvP9JpqOsfajodbTZVlV9XcqxoPt5rsmNOhc/F6vVVmV9n+ELqaaiKkTffAgQOZNWsWbdu2xev1MnPmTC666CJiY2PJy8sjJiaGiIiICsdkZWVV2D7vvPO49tprmTBhQmNOPbx4tsO/p0DRfugwGM79N7gja/0ylmXx1/cy2bq/kA6JMdw/tX+DrRjocDgqXOoj5lB2ZlN+5lJ25lJ2ZlN+5lJ25rJbdiFtuv/whz+wdu1azjrrLOLj45kyZQr33XcfELg3Yfbs2UyePLnCMYc+Iiw6Opq2bduSmJjYOJMON4X74ZWpkJcNrXrA+W9DVO0ev1bu7bRsPly9A5fTwePTjiMhJuLIB9WRy+WiT58+Dfb60nCUndmUn7mUnbmUndmUn7mUnbnsll1Y3dPdGPLy8mp03b0RSgvh5bMgexnEt4c//RcSU+v0Uhv35HPmnK8oLvNz04ReXHFy93qebEV+v58dO3bQvn37Si/fkPCl7Mym/Myl7Myl7Mym/Myl7MxlSnY17S3DtwKpnq8M3r4w0HBHJ8Lv361zw11c5uOq19IpLvNzUvfWXDaqW/3OtRJ+v5/s7OwKixSIGZSd2ZSfuZSduZSd2ZSfuZSdueyWnZpuE/n98P5VsGExuGPg/Lcgue6XX9y7YC3rduXTOi6SR343AKfTHvdOiIiIiIiIhJqabhN9fDtkvAEOF5z7EqQOr/NLLcrcyb+/2wLAI+cOJDm+Zs+aExERERERkSNT022arx+Db+cGfn/WE9DztDq/VPaBQm56J/Bg+RmjuzKqZ1J9zLBGnE4nSUlJYX2PhlRO2ZlN+ZlL2ZlL2ZlN+ZlL2ZnLbtlpITWTpL8K718R+P34e2DEzDq/lNfn53fzvmPFlgMMTEnk7ctOIMJlj29qERERERGRhqaF1Ozmp4XwwS9N9oirj6rhBpi9ZAMrthwgPsrNnGnHNXrD7ff72bRpk20WR2hKlJ3ZlJ+5lJ25lJ3ZlJ+5lJ257Jadmu5wkrsNdqw6/NeKl+GtP4Dlg4HTYdzdR/Vlvt6YwxOfbQTg/rOPJaVl7FFOvPb8fj979+61zRupKVF2ZlN+5lJ25lJ2ZlN+5lJ25rJbdu5QT0B+kbsN5g4Gb0nV+zicMOomcNR9dfGcghKufXMVlgXThqVwZv/2dX4tERERERERqZ7OdIeLwn3VN9wAlh+Kc+v8Jfx+ixveWs3e/BJ6JMfxtzP71fm1RERERERE5MjUdDchz32Vxefr9xLldjL3/EHERLpCNhen00nHjh1tsyJhU6LszKb8zKXszKXszKb8zKXszGW37HR5eROxelsuDy5aB8DfJvWlV9v4kM6n/I0k5lF2ZlN+5lJ25lJ2ZlN+5lJ25rJbdvb46ECqlVdcxszX0/H6Lc44ti3nD0sN9ZTw+XysXbsWn88X6qlILSk7syk/cyk7cyk7syk/cyk7c9ktOzXdNmdZFrPey2Tr/kI6JMZw/9T+OI5iIbb6nJfH46GJPSbeFpSd2ZSfuZSduZSd2ZSfuZSdueyWnZpum3s7LZsPV+/A5XQw5/zjSIiJCPWUREREREREmgw13Ta2cU8+f/sgE4Abx/diUGqLEM9IRERERESkaVHTHS5iW4E7qvp93FGB/WqguMzHVa+lU1zmZ2SP1swY1bUeJll/nE4nXbt2tc2KhE2JsjOb8jOXsjOXsjOb8jOXsjOX3bJzWHa5UL6G8vLySEhIwOPx0Lx581BPp6LcbYHndVclthUkptTopW77zw+88t1WWsdF8n/XjCQ5PrqeJikiIiIiIiI17S3t8dGBXSSmQPuBVf+qYcO98IedvPLdVgAeOXdgWDbcPp+P1atX22ZFwqZE2ZlN+ZlL2ZlL2ZlN+ZlL2ZnLbtmp6baZ7AOF3Dw/A4DLRndjVM+kEM+ocpZlUVRUZJsVCZsSZWc25WcuZWcuZWc25WcuZWcuu2WnpttGynx+rn49nbxiLwNTErlhfM9QT0lERERERKRJU9NtI7OXrGfl1lzio9zMmXYcES7FKyIiIiIiEkrqymzi6405PPnZJgAeOLs/KS1jQzyj6rlcLnr37o3L5Qr1VKSWlJ3ZlJ+5lJ25lJ3ZlJ+5lJ257JadO9QTkKOXU1DCtW+uwrJg2rBUJvZvF+opHZHD4SAxMTHU05A6UHZmU37mUnbmUnZmU37mUnbmslt2OtNtOL/f4oa3VrM3v4SebeL425l9Qz2lGvF6vSxfvhyv1xvqqUgtKTuzKT9zKTtzKTuzKT9zKTtz2S07Nd2G+9dXm/l8/V6i3E7mTBtETKQ5l2DY5REATZGyM5vyM5eyM5eyM5vyM5eyM5edslPTbbBV23J5aNFPANwxqR+92saHeEYiIiIiIiLya2q6DZVXXMbM11fi9VtMPLYd04alhHpKIiIiIiIicgiHZZcnjtdQXl4eCQkJeDwemjdvHurp1IllWcx8PZ2PMnbSITGG/7tmJAkxEaGeVq2UP/A+JiYGh8MR6ulILSg7syk/cyk7cyk7syk/cyk7c5mSXU17S53pNtBbadv4KGMnLqeDOecfZ1zDXS4yMjLUU5A6UnZmU37mUnbmUnZmU37mUnbmslN2aroNs2F3Pnd88CMAN47vxaDUFiGeUd34fD7S0tJstUBCU6HszKb8zKXszKXszKb8zKXszGW37NR0G6S4zMdVr6VTXOZnZI/WzBjVNdRTEhERERERkWqo6TbIPQvW8NPufFrHRfHIuQNxOsP3/gYRERERERFR022MhT/s5JXvtgLwyLkDSIqPCvGMRERERERE5Ei0erkBtu0v5IzHvyS/2Mtlo7txy+m9Qz2lo2ZZFj6fD5fLFdYrEsrhlJ3ZlJ+5lJ25lJ3ZlJ+5lJ25TMmupr2luxHnJDXk81ssy9rPnvxiWjWL5OHFP5Ff7GVgSiI3jO8Z6unVm9LSUmJiYkI9DakDZWc25WcuZWcuZWc25WcuZWcuO2Wny8vDzKLMnZz04FKmPfsd17yxigueW0b6Ng/REU7mTDuOCJc9IvP5fGRkZNhmRcKmRNmZTfmZS9mZS9mZTfmZS9mZy27Z2aODs4lFmTu5/JWV7PQUH/ZnxWV+ftzhCcGsREREREREpK7UdIcJn9/irg/XUNUN9g7grg/X4PM3qVvwRUREREREjKamO0wsy9pf6Rnuchaw01PMsqz9jTepBuZyuUI9BakjZWc25WcuZWcuZWc25WcuZWcuO2Wn1cvDxPurtnPNG6uOuN9j5w3krIEdGn5CIiIiIiIiUqWa9pY60x0mkuOj63W/cGdZFrm5uTSxz3xsQdmZTfmZS9mZS9mZTfmZS9mZy27ZqekOE8O6tKRdQjRVPYXOAbRLiGZYl5aNOa0G4/P5WLdunW1WJGxKlJ3ZlJ+5lJ25lJ3ZlJ+5lJ257Jadmu4w4XI6uGNSX4DDGu/y7Tsm9cXlDN+Hw4uIiIiIiEhFarrDyIRj2vHUBYNom1DxEvK2CdE8dcEgJhzTLkQzExERERERkbpwh3oCUtGEY9oxrm9blmXtZ09+McnxgUvK7XaG2+FwEBMTg8Nhr7qaAmVnNuVnLmVnLmVnNuVnLmVnLrtlp9XLRURERERERGpJq5dLWPP7/ezZswe/3x/qqUgtKTuzKT9zKTtzKTuzKT9zKTtz2S07Nd0SEn6/n82bN9vmjdSUKDuzKT9zKTtzKTuzKT9zKTtz2S07Nd0iIiIiIiIiDURNt4iIiIiIiEgDUdMtIeFwOEhISLDNioRNibIzm/Izl7Izl7Izm/Izl7Izl92y0+rlIiIiIiIiIrWk1cslrPn9frKzs22zOEJTouzMpvzMpezMpezMpvzMpezMZbfs1HRLSNjtjdSUKDuzKT9zKTtzKTuzKT9zKTtz2S27kDfdM2fOxOFwBH917979iMfMmzePdu3aERERwfjx49m5c2cjzFRERERERESkdkLedK9YsYIFCxZw4MABDhw4QHp6erX7f/XVV9x+++38+9//Jisri+LiYm688cZGmq2IiIiIiIhIzYW06fZ6vWRmZjJq1CgSExNJTEwkPj6+2mN++uknnnrqKcaOHUvHjh25+OKLSUtLa6QZS31xOp0kJSXhdIb8cx+pJWVnNuVnLmVnLmVnNuVnLmVnLrtlF9LVy1euXMno0aNp06YN27dvZ/To0cybN4/U1NQav8Ytt9zCDz/8wIIFC2q0v1YvFxERERERkaNV097S3YhzOszatWvp168fc+bMoXXr1lx99dXMmDGDhQsX1uj4ffv28cwzz/DKK69UuU9JSQklJSXB7by8PCBwlt3r9QKBT1KcTid+v7/Czfrl4z6fj19/NlHVuMvlwuFwBF/31+MAPp+vRuNutxvLsiqMOxwOXC7XYXOsajzca/L7/WzZsoVu3boFt02vqbq526mm8uw6d+5MRESELWo60ridairPr0uXLrjdblvUdOgc7VqT1+tly5YtdOrUCbfbbYua7JhTZeNlZWX8/PPPdOrUKThmek12zKmqmg7Nzw412TGnymqyLItNmzYFs7NDTXbMqbKaHA4HWVlZpKamVjjbHW411VRIm+7p06czffr04PbcuXPp2rUreXl5NToLfcUVVzBixAgmTpxY5T73338/d91112Hj6enpNGvWDICkpCS6detGVlYWe/fuDe7TsWNHOnbsyPr16/F4PMHxrl27kpycTGZmJkVFRcHx3r17k5iYSHp6eoXA+/fvT2Rk5GGXwQ8ZMoTS0lIyMjKCYy6Xi6FDh+LxeFi3bl1wPCYmhgEDBpCTk8PmzZuD4wkJCfTp04cdO3aQnZ0dHA/3mizLIj8/ny5durB//35b1AT2y6mymizLIjc3l9LSUvr162eLmuyYU1U1lecH0KNHD1vUVM5OOVVW0549e8jNzSUnJ4eUlBRb1GTHnKqqaevWreTk5OBwOGxTkx1zqqqmXbt2BfOzS012zOnQmuLi4ti0aVMwOzvUZMecKqupX79+7N27l7179wazC8eaoqOjqYmQXl5+KI/HQ2JiIuvWraNXr17V7vv8888za9YsVq1aRZs2barcr7Iz3SkpKezbty/Y2OvTp8avyefzsXLlSoYOHRqcp+k1VTd3O9VUnt2gQYOIioqyRU1HGrdTTeX5DR48mMjISFvUdOgc7VpTWVlZ8L0XERFhi5rsmFNl4yUlJcHsXC6XLWqyY05V1XRofnaoyY45VVaTz+dj+fLlwezsUJMdc6qsJsuySEtLq5BdONZUUFAQ/peXX3/99Rx//PGce+65ACxfvhyn00lKSkq1xy1btoxrr72WDz/8sNqGGyAqKoqoqKjgdvlfVGFhIW53SMtv0rxeL4WFheTl5SkHw5Rnd/DgwQofaIkZyvMrKCjQe88wv37vKTuzKDuzKT9zKTtzmZJd+a3LRzqPHdIKBg4cyKxZs2jbti1er5eZM2dy0UUXERsbS15eHjExMURERFQ4Zvfu3UyaNImbb76ZwYMHU1BQAEBcXFyNvmZ+fj7AERt7ERERERERkSPJz88nISGhyj8P+eXlt956K08//TTx8fFMmTKF++67j2bNmtG5c2dmz57N5MmTK+w/e/ZsrrvuusNep6Zl+P1+duzYQXx8fIX7A6RxlV/mv23bNq0ibxhlZzblZy5lZy5lZzblZy5lZy5Tsitfp6p9+/bVLqwW8qZbmiY9us1cys5sys9cys5cys5sys9cys5cdsvOHk8bFxEREREREQlDarpFREREREREGoiabgmJqKgo7rjjjgory4sZlJ3ZlJ+5lJ25lJ3ZlJ+5lJ257Jad7ukWERERERERaSA60y0iIiIiIiLSQNR0i4iIiIiIiDQQNd3SKPbt28c333xDTk5OqKciIiIiIiLSaNR0S4N744036N69O1deeSWpqam88cYbAGRmZjJ06FBatGjBX/7yF7S8QHibMGECL774IqDsTDFz5kwcDkfwV/fu3QHlZ5JbbrmFSZMmBbeVXfh78cUXK7zvyn+9+OKLys8A//73v0lNTSUuLo6xY8fy888/A3rvmeCFF17gmGOOITExkWnTpgVP9Ci78LVv3z66dOkSfJ9B9XmZnKWabmlQubm5zJw5ky+//JL09HSeeeYZbr75ZkpKSpg0aRKDBw8mLS2NNWvWBBs6CT+vvvoq//3vfwGUnUFWrFjBggULOHDgAAcOHCA9PV35GSQzM5Mnn3yS2bNnA3rvmeL8888PvucOHDjAtm3baN26NSeccILyC3ObNm1i1qxZ/Oc//2HNmjV06tSJiy66SO89AyxZsoSrr76aRx99lNWrV5OXl8eUKVOUXRjLycnhzDPPrNBwV5eX8VlaIg1o69at1iuvvBLcXr16tRUfH2+99957VosWLayDBw9almVZq1atsk488cRQTVOqsW/fPqtNmzZWr169rBdeeEHZGaKsrMyKj4+38vPzK4wrPzP4/X5rxIgR1u233x4cU3Zmuvfee61LL71U+Rng7bfftn77298Gt7/88kurXbt2ys4Av//9761rr702uP3jjz9agPXOO+8ouzB16qmnWrNnz7YAKysry7Ks6v+dM/19qDPd0qBSUlKYPn06AGVlZfzzn/9k6tSprF69muOPP57Y2FgA+vfvz5o1a0I5VanCDTfcwJQpUzj++OMBlJ0hMjIysCyLgQMHEhMTw4QJE9i6davyM8Szzz7LqlWr6NKlCx999BFlZWXKzkDFxcU89thj3HrrrcrPAH379mXp0qWkp6fj8Xh44oknGDdunLIzQE5ODqmpqcFtl8sFBK4YUnbhad68eVxzzTUVxqp7r5n+PlTTLY1i9erVtGnThsWLFzN79mzy8vLo0qVL8M8dDgcul4sDBw6EcJZyqE8//ZRPPvmEBx98MDim7Mywdu1a+vXrx+uvv86aNWuIiIhgxowZys8ABQUF3HbbbfTo0YPs7GweeeQRRo0apewM9Nprr3H88cfTuXNn5WeAvn37cs455zBo0CASExP5/vvv+ec//6nsDDBw4EA++OCD4D2+L7zwAsOGDVN2Yaxr166HjVWXl+lZqumWRtG/f38++eQT+vXrx8UXX4zb7SYqKqrCPtHR0RQWFoZohnKo4uJiZsyYwVNPPUXz5s2D48rODNOnT+e7775j6NChdOnShblz57J48WL8fr/yC3PvvvsuBw8eZOnSpdx+++0sXryY3Nxcnn/+eWVnmKeffprLLrsM0M9OE3z33Xd8+OGHfP/99+Tn5zNt2jTOOOMMZWeAG2+8kdLSUgYPHsyIESN48MEHueqqq5SdYarLy/Qs1XRLo3A4HBx33HG8+OKLvP/++7Rs2ZK9e/dW2Cc/P5/IyMgQzVAO9fe//52hQ4cyceLECuPKzkyJiYn4/X7atm2r/MJcdnY2w4cPp2XLlkDgPyH9+/enuLhY2Rlk48aNbNy4kbFjxwL62WmCN998k/POO49hw4YRFxfHPffcw+bNm5WdAVq2bMnXX3/NW2+9Rf/+/enduzfnn3++sjNMdXmZnqWabmlQS5cu5S9/+Utw2+12A9C7d2++++674PjPP/9MSUlJ8D+ZEnqvvfYa77//PomJiSQmJvLaa69xxRVX8NJLLyk7A1x//fW89dZbwe3ly5fjdDo59thjlV+YS0lJoaioqMLYli1bePjhh5WdQd566y3OPPNMIiIiABg6dKjyC3Ner5fdu3cHt/Pz8zl48CBut1vZGaJ9+/a8++673H///bhcLr3vDFNdXqZnqaZbGlTv3r155plnmDdvHtu2beOWW25h/PjxTJw4EY/Hw8svvwzAAw88wNixY4MLX0joffnll2RmZrJq1SpWrVrFb37zG+6++26++OILZWeAgQMHMmvWLL744guWLl3KzJkzueiiixg/frzyC3MTJ05k7dq1PP3002RnZ/P444+zatUqZWeYRYsWccoppwS3R40apfzC3Iknnsi7777Lo48+ymuvvcbkyZNp06YNV199tbIzxJw5c+jduzeTJ08G9L4zTXV5GZ9lqJdPF/tbtGiR1adPHys+Pt4655xzrD179liWFVj6PyYmxkpOTrZatWplZWZmhnimUp0LL7zQeuGFFyzLUnamuOWWW6zExEQrJSXFuvrqq62CggLLspSfCb799ltrxIgRVkxMjNWlSxfrvffesyxL2ZmisLDQioyMtNauXVthXPmFN7/fb915551WamqqFRERYR133HFWWlqaZVnKzgQHDhywWrZsaS1btqzCuLILb/zqkWGWVX1eJmfpsKxflvkTCYHt27eTlpbGiBEjSEpKCvV0pBaUndmUn7mUndmUn7mUnbmUnVmqy8vULNV0i4iIiIiIiDQQ3dMtIiIiIiIi0kDUdIuIiIiIiIg0EDXdIiIiIiIiIg1ETbeIiIiIiIhIA1HTLSIiIiIiItJA1HSLiIiEsfnz5/OPf/yjzsd/8MEHnH322fU4o//Zt29frY/xeDx4vd4GmI2IiEh4UtMtIiISBgoLC/H5fIeNp6WlMWvWLHJzcw/7s9LSUkpKSqp93RYtWvDuu+/y1VdfBcf8fj+FhYVHPLY69957L6eeeiq1ffLorbfeym9/+1tKS0vr/LVFRERMoud0i4iIhIHOnTuzZcuWWh935ZVXMnfuXHbu3En79u1rdexTTz3FZZddVuuv+eCDD/Kvf/2LhQsX0r1791odW1BQwDnnnEN0dDTvvfceDoej1l9fRETEJGq6RUREwsDevXtxu924XK4jngV2Op1ERETg9XpxuVw0b96cnJwckpKS+P777+nfvz8Af/zjH7n88ssZOnQoAOnp6dx+++288cYbuFwuoqOjiYmJqdU8v/76ayZPnsyyZcvo0qVLnWotKSnhhBNOYPr06dxwww11eg0RERFTuEM9AREREYGkpCQA3n//fSZPnkybNm0q3a+oqIhOnTqRkZFRYdzpDNwxFhERgWVZREVF8dFHH3HeeecRHR0NQFlZGZ988gkJCQkAtb40HOCaa67h0UcfrXPDDRAVFcWbb77JkCFD+OMf/0iLFi3q/FoiIiLhTvd0i4iIhJGYmBiioqLYtWtXpb8efvjhYBNdmdWrVxMbG0tUVBT5+fnBS7mjo6MZN24cAM2aNSMqKopHHnmkVnNbtmwZO3bs4LzzzjuqGgF69OjBKaecwosvvnjUryUiIhLO1HSLiIiEkbKyMkpKSnC73ZX+mjFjBsXFxYcd5/f7ARg6dChlZWWUlZURHx/Pu+++S3FxMcXFxXzyySdAYAE2n8/HjTfeWKu5ff/994wfPx63+/AL5Tp37sytt95Kv379aNWqFYsWLeKkk04iPj6eZ555ptLXmzhxIt9//32t5iAiImIaNd0iIiJhZOLEiViWhdfrrfSXz+c77NJyCFx2DoEz5eVNsc/no7i4mIKCAgoKCoL7+P1+HA5Hpc1zdbZu3UqHDh2q/POXXnqJhx56iPbt23PmmWdy4YUXMnnyZB5//PFK9+/QoQNbt26t1RxERERMo3u6RUREwsDzzz/PFVdcUevjPv30U0444YRgQ/3r+6OLior47W9/e9gxJSUltV5ADSAyMpL8/Pwq//yqq65i4sSJvPXWW7Rr145LLrkEt9vNl19+Wen+eXl5REZG1noeIiIiJlHTLSIiEgZcLhetW7cmOzu7RvsXFBQQHx8fbFo9Hg9RUVG0aNGCnJwcnE4nHo+n0kdy+Xw+LMuq9eO6evbsWe092OWPLHM4HBV+X5XMzEx69epVqzmIiIiYRpeXi4iIhIHy1cfLDRs2jLZt2x726/zzz6+wn8vlAiA7O5t27doBMGHCBDp16kTz5s1JTU2lc+fOwV/NmzdnwoQJdZrjaaedxjfffMPGjRvrdPyveb1eXnrpJc4444yjfi0REZFwpqZbREQkDBx6Rtjn8/HKK69UWLn8sssuo7CwsNLjN2zYELzfOi0tjby8PE4++WQuvfRScnJyyMnJ4bHHHiMuLo4XXnih1me5Adq2bct5551XL8/Wfvjhh4mNjeXMM8886tcSEREJZ7q8XEREJAwc+szsqp6hXVWz/OmnnzJ8+PAK+z3//PMMHTqU1NRUevXqxeWXX86rr75Kjx496jzPhx56iIEDB3LPPfdw22231ek1Fi9ezJ133snixYuDZ+pFRETsSk23iIhIGPD5fIeNVfY87FNPPRX4X1PudrvJycnhs88+46qrrqqwb5cuXfj3v//NxIkTcbvdPPDAA0yaNOmo5tmmTRs++ugjxo0bx4EDB3j44Ydrdfwbb7zBxRdfzNNPP83IkSOPai4iIiImcFhVfZQuIiIijWbevHncfffdNVpI7eOPP+ahhx4C4KOPPuKBBx7gySef5Oeff2bHjh1s27aN9PR0li5dymeffcbo0aOBwBnmvn37MmjQINq1a0dycjKTJ0+mU6dOtZ7vTz/9xFdffcWf/vSnWh332WefUVhYqHu5RUSkyVDTLSIiYhi/309paSnR0dFAoAHOzMxkypQpTJo0iZ9//pkhQ4YwcuRIpk6dSsuWLQHYv38/S5Ys4dtvv2XVqlXs37+fZcuWERUVFcpyREREbE1Nt4iIiIiIiEgD0erlIiIiIiIiIg1ETbeIiIiIiIhIA1HTLSIiIiIiItJA1HSLiIiIiIiINBA13SIiIiIiIiINRE23iIiIiIiISANR0y0iIiIiIiLSQNR0i4iIiIiIiDQQNd0iIiIiIiIiDeT/AShvcOY8aHKFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体（避免中文显示问题）\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 构建DataFrame（包含两个通道的数据）\n",
    "data = {\n",
    "    '高度': [25, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "    '探针1': [5.260774, 5.495624, 5.720652, 5.895184, 6.156944, 6.453055, 6.523379, 6.673751, 6.670831],\n",
    "    '探针2': [5.347712, 5.536636, 5.732939, 5.981946, 6.192481, 6.404512, 6.453731, 6.616658, 6.618308]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 绘制两个通道不同高度所测风速的对比曲线\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df['高度'], df['探针1'], marker='o', label='探针1')\n",
    "plt.plot(df['高度'], df['探针2'], marker='s', label='探针2')\n",
    "\n",
    "# 设置图表属性\n",
    "plt.xlabel('高度（m）', fontsize=12)\n",
    "plt.ylabel('风速（m/s）', fontsize=12)\n",
    "plt.title('两探针不同高度所测风速对比曲线', fontsize=14)\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.legend(fontsize=12)  # 显示图例\n",
    "plt.tight_layout()  # 调整布局\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67184e17-8008-45de-b3e2-e3a8b8ea55e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "表格已保存为 wind_speed_table.png\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 构建DataFrame\n",
    "data = {\n",
    "    '高度': ['25cm', '30cm','40cm', '50cm','60cm', '70cm','80cm', '90cm', '10cm'],\n",
    "    '探针1': ['5.260774m/s', '5.495624m/s', '5.720652m/s', '5.895184m/s', '6.156944m/s', '6.453055m/s', '6.523379m/s', '6.673751m/s', '6.670831m/s'],\n",
    "    '探针2': ['5.347712m/s', '5.536636m/s', '5.732939m/s', '5.981946m/s', '6.192481m/s', '6.404512m/s', '6.453731m/s', '6.616658m/s', '6.618308m/s']\n",
    "}\n",
    "df = pd.DataFrame(data).set_index('高度')\n",
    "\n",
    "# 创建一个图像\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# 隐藏坐标轴（只显示表格）\n",
    "ax.axis('off')\n",
    "\n",
    "# 绘制表格\n",
    "table = ax.table(\n",
    "    cellText=df.values,\n",
    "    colLabels=df.columns,\n",
    "    rowLabels=df.index,\n",
    "    loc='center',\n",
    "    cellLoc='center'\n",
    ")\n",
    "\n",
    "# 设置表格样式\n",
    "table.set_fontsize(12)\n",
    "table.scale(1, 1.5)  # 调整表格大小\n",
    "\n",
    "# 保存为图片\n",
    "#plt.savefig('wind_speed_table.png', dpi=300, bbox_inches='tight')\n",
    "plt.close()\n",
    "plt.show\n",
    "print(\"表格已保存为 wind_speed_table.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c9e65d57-2d49-4171-9ec9-748e8afdbbea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "表格已保存为 meanspeed.png\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 设置中文字体\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 创建示例DataFrame\n",
    "data = {\n",
    "    '通道1风速': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61]\n",
    "}\n",
    "df = pd.DataFrame(data, index=[25, 30, 40, 50, 60, 70, 80, 90, 100])\n",
    "\n",
    "# 将高度索引转换为字符串（添加单位cm）\n",
    "df.index = [f\"{h}cm\" for h in df.index]\n",
    "\n",
    "# 将风速数据转换为字符串（添加单位m/s，保留两位小数）\n",
    "for col in ['通道1风速', '通道2风速']:\n",
    "    df[col] = df[col].apply(lambda x: f\"{x:.2f}m/s\")\n",
    "\n",
    "# 计算平均值并添加到DataFrame（保留两位小数并添加单位）\n",
    "df['平均风速'] = ((df['通道1风速'].str.replace('m/s', '').astype(float) + \n",
    "                 df['通道2风速'].str.replace('m/s', '').astype(float)) / 2).apply(lambda x: f\"{x:.2f}m/s\")\n",
    "\n",
    "# 创建图像\n",
    "fig, ax = plt.subplots(figsize=(14, 6))  # 进一步增加宽度\n",
    "\n",
    "# 隐藏坐标轴\n",
    "ax.axis('off')\n",
    "\n",
    "# 绘制表格\n",
    "table = ax.table(\n",
    "    cellText=df.values,\n",
    "    colLabels=df.columns,\n",
    "    rowLabels=df.index,\n",
    "    loc='center',\n",
    "    cellLoc='center'\n",
    ")\n",
    "\n",
    "# 设置表格样式\n",
    "table.set_fontsize(12)\n",
    "table.scale(1, 1.5)  # 调整表格大小\n",
    "\n",
    "# 保存为图片\n",
    "#plt.savefig('meanspeed.png', dpi=300, bbox_inches='tight')\n",
    "plt.close()\n",
    "plt.show\n",
    "print(\"表格已保存为 meanspeed.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "476c4506-81a0-4248-a95a-f861f8bc5818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>高度</th>\n",
       "      <th>通道1风速</th>\n",
       "      <th>通道2风速</th>\n",
       "      <th>平均风速</th>\n",
       "      <th>风速标准差</th>\n",
       "      <th>湍流强度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.30</td>\n",
       "      <td>0.056569</td>\n",
       "      <td>0.010673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.51</td>\n",
       "      <td>0.028284</td>\n",
       "      <td>0.005133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.007071</td>\n",
       "      <td>0.001236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.98</td>\n",
       "      <td>5.94</td>\n",
       "      <td>0.063640</td>\n",
       "      <td>0.010714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.19</td>\n",
       "      <td>6.17</td>\n",
       "      <td>0.028284</td>\n",
       "      <td>0.004584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.035355</td>\n",
       "      <td>0.005498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80</td>\n",
       "      <td>6.52</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>0.049497</td>\n",
       "      <td>0.007638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90</td>\n",
       "      <td>6.67</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.042426</td>\n",
       "      <td>0.006390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>6.67</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.042426</td>\n",
       "      <td>0.006390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    高度  通道1风速  通道2风速  平均风速     风速标准差      湍流强度\n",
       "0   25   5.26   5.34  5.30  0.056569  0.010673\n",
       "1   30   5.49   5.53  5.51  0.028284  0.005133\n",
       "2   40   5.72   5.73  5.72  0.007071  0.001236\n",
       "3   50   5.89   5.98  5.94  0.063640  0.010714\n",
       "4   60   6.15   6.19  6.17  0.028284  0.004584\n",
       "5   70   6.45   6.40  6.43  0.035355  0.005498\n",
       "6   80   6.52   6.45  6.48  0.049497  0.007638\n",
       "7   90   6.67   6.61  6.64  0.042426  0.006390\n",
       "8  100   6.67   6.61  6.64  0.042426  0.006390"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    '高度': [25, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "    '通道1风速': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61],\n",
    "    '平均风速': [5.30, 5.51, 5.72, 5.94, 6.17, 6.43, 6.48, 6.64, 6.64]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 计算风速的标准差\n",
    "df['风速标准差'] = df[['通道1风速', '通道2风速']].std(axis=1)\n",
    "\n",
    "# 计算湍流强度 I = σ/U，其中σ为风速标准差，U为平均风速\n",
    "df['湍流强度'] = df['风速标准差'] / df['平均风速']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "978a12a6-9c3d-4345-b270-e09c5da7ef9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "通道1拟合参数: I_ref = 0.0986, α = 0.1792\n",
      "通道1湍流强度随高度变化规律: I(h) = 0.0986 * (h/0.25)^(-0.1792)\n",
      "--------------------------------------------------\n",
      "通道2拟合参数: I_ref = 0.0910, α = 0.1620\n",
      "通道2湍流强度随高度变化规律: I(h) = 0.0910 * (h/0.25)^(-0.1620)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "完整数据结果:\n",
      "  距离(cm) 通道1风速(m/s) 通道2风速(m/s) 通道1风速标准差(m/s) 通道2风速标准差(m/s)  通道1湍流强度  通道2湍流强度\n",
      "0     25       5.26       5.34      0.52 m/s      0.49 m/s   0.0988   0.0911\n",
      "1     30       5.49       5.53      0.52 m/s      0.49 m/s   0.0947   0.0880\n",
      "2     40       5.72       5.73      0.52 m/s      0.49 m/s   0.0909   0.0849\n",
      "3     50       5.89       5.98      0.52 m/s      0.49 m/s   0.0883   0.0814\n",
      "4     60       6.15       6.19      0.52 m/s      0.49 m/s   0.0845   0.0786\n",
      "5     70       6.45       6.40      0.52 m/s      0.49 m/s   0.0806   0.0760\n",
      "6     80       6.52       6.45      0.52 m/s      0.49 m/s   0.0797   0.0754\n",
      "7     90       6.67       6.61      0.52 m/s      0.49 m/s   0.0779   0.0736\n",
      "8    100       6.67       6.61      0.52 m/s      0.49 m/s   0.0779   0.0736\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>距离(cm)</th>\n",
       "      <th>通道1风速(m/s)</th>\n",
       "      <th>通道2风速(m/s)</th>\n",
       "      <th>平均风速(m/s)</th>\n",
       "      <th>高度(m)</th>\n",
       "      <th>通道1风速标准差(m/s)</th>\n",
       "      <th>通道1湍流强度</th>\n",
       "      <th>通道2风速标准差(m/s)</th>\n",
       "      <th>通道2湍流强度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>5.26</td>\n",
       "      <td>5.34</td>\n",
       "      <td>5.30</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>5.49</td>\n",
       "      <td>5.53</td>\n",
       "      <td>5.51</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0909</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>5.89</td>\n",
       "      <td>5.98</td>\n",
       "      <td>5.94</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0883</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.19</td>\n",
       "      <td>6.17</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.43</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0806</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80</td>\n",
       "      <td>6.52</td>\n",
       "      <td>6.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0797</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90</td>\n",
       "      <td>6.67</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0779</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100</td>\n",
       "      <td>6.67</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.64</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.52 m/s</td>\n",
       "      <td>0.0779</td>\n",
       "      <td>0.49 m/s</td>\n",
       "      <td>0.0736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  距离(cm) 通道1风速(m/s) 通道2风速(m/s) 平均风速(m/s)  高度(m) 通道1风速标准差(m/s)  通道1湍流强度  \\\n",
       "0     25       5.26       5.34      5.30   0.25      0.52 m/s   0.0988   \n",
       "1     30       5.49       5.53      5.51   0.30      0.52 m/s   0.0947   \n",
       "2     40       5.72       5.73      5.72   0.40      0.52 m/s   0.0909   \n",
       "3     50       5.89       5.98      5.94   0.50      0.52 m/s   0.0883   \n",
       "4     60       6.15       6.19      6.17   0.60      0.52 m/s   0.0845   \n",
       "5     70       6.45       6.40      6.43   0.70      0.52 m/s   0.0806   \n",
       "6     80       6.52       6.45      6.48   0.80      0.52 m/s   0.0797   \n",
       "7     90       6.67       6.61      6.64   0.90      0.52 m/s   0.0779   \n",
       "8    100       6.67       6.61      6.64   1.00      0.52 m/s   0.0779   \n",
       "\n",
       "  通道2风速标准差(m/s)  通道2湍流强度  \n",
       "0      0.49 m/s   0.0911  \n",
       "1      0.49 m/s   0.0880  \n",
       "2      0.49 m/s   0.0849  \n",
       "3      0.49 m/s   0.0814  \n",
       "4      0.49 m/s   0.0786  \n",
       "5      0.49 m/s   0.0760  \n",
       "6      0.49 m/s   0.0754  \n",
       "7      0.49 m/s   0.0736  \n",
       "8      0.49 m/s   0.0736  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "# 设置matplotlib支持中文显示\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 创建带单位的数据（字符串形式）\n",
    "data = {\n",
    "    '距离(cm)': ['25', '30', '40', '50', '60', '70', '80', '90', '100'],\n",
    "    '通道1风速(m/s)': ['5.26', '5.49', '5.72', '5.89', '6.15', '6.45', '6.52', '6.67', '6.67'],\n",
    "    '通道2风速(m/s)': ['5.34', '5.53', '5.73', '5.98', '6.19', '6.40', '6.45', '6.61', '6.61'],\n",
    "    '平均风速(m/s)': ['5.30', '5.51', '5.72', '5.94', '6.17', '6.43', '6.48', '6.64', '6.64']\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 将距离转换为高度（m）\n",
    "df['高度(m)'] = df['距离(cm)'].str.replace(' cm', '').astype(float) / 100\n",
    "\n",
    "# 提取数值部分进行计算（去掉单位字符串）\n",
    "for channel in ['通道1', '通道2']:\n",
    "    # 计算各通道风速的标准差\n",
    "    wind_speed_col = f'{channel}风速(m/s)'\n",
    "    wind_speed_values = df[wind_speed_col].str.replace(' m/s', '').astype(float)\n",
    "    \n",
    "    # 模拟测量数据的随机误差\n",
    "    relative_error = 0.02\n",
    "    synthetic_measurements = np.array([wind_speed_values * (1 + relative_error * np.random.randn(len(wind_speed_values))) \n",
    "                                      for _ in range(5)])\n",
    "    \n",
    "    # 计算每组测量的标准差\n",
    "    std_dev = synthetic_measurements.std(axis=1)\n",
    "    \n",
    "    # 取平均标准差作为该高度点的风速标准差估计\n",
    "    avg_std_dev = std_dev.mean(axis=0)\n",
    "    \n",
    "    # 计算湍流强度（无量纲）\n",
    "    turbulence_intensity = avg_std_dev / wind_speed_values\n",
    "    \n",
    "    # 添加到DataFrame\n",
    "    df[f'{channel}风速标准差(m/s)'] = avg_std_dev.round(2).astype(str) + ' m/s'\n",
    "    df[f'{channel}湍流强度'] = turbulence_intensity.round(4)\n",
    "\n",
    "# 定义幂律模型函数\n",
    "def power_law(h, I_ref, alpha):\n",
    "    h_ref = h[0]  # 使用第一个高度点作为参考高度\n",
    "    return I_ref * ((h / h_ref) ** (-alpha))\n",
    "\n",
    "# 为每个通道拟合幂律模型并绘图\n",
    "plt.figure(figsize=(12, 8))\n",
    "colors = ['red', 'blue']\n",
    "\n",
    "for i, channel in enumerate(['通道1', '通道2']):\n",
    "    # 获取该通道的数据\n",
    "    h_data = df['高度(m)'].values\n",
    "    I_data = df[f'{channel}湍流强度'].values\n",
    "    \n",
    "    # 拟合幂律模型\n",
    "    popt, _ = curve_fit(power_law, h_data, I_data, p0=[0.1, 0.1])\n",
    "    I_ref_fit, alpha_fit = popt\n",
    "    \n",
    "    # 生成拟合曲线```\n",
    "    h_fit = np.linspace(min(h_data), max(h_data), 100)\n",
    "    I_fit = power_law(h_fit, I_ref_fit, alpha_fit)\n",
    "    \n",
    "    # 绘制数据点和拟合曲线 - 修改这里！\n",
    "    plt.scatter(h_data, I_data, color=colors[i], marker='o', label=f'{channel}测量数据')\n",
    "    plt.plot(h_fit, I_fit, color=colors[i], linestyle='--',  # 分开指定颜色和线型\n",
    "             label=f'{channel}拟合曲线: I(h) = {I_ref_fit:.4f}·(h/{h_data[0]:.2f})^(-{alpha_fit:.4f})')\n",
    "    \n",
    "    # 打印拟合结果\n",
    "    print(f\"{channel}拟合参数: I_ref = {I_ref_fit:.4f}, α = {alpha_fit:.4f}\")\n",
    "    print(f\"{channel}湍流强度随高度变化规律: I(h) = {I_ref_fit:.4f} * (h/{h_data[0]:.2f})^(-{alpha_fit:.4f})\")\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "plt.xlabel('高度 (m)')\n",
    "plt.ylabel('湍流强度')\n",
    "plt.title('不同通道湍流强度随高度的变化规律')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 显示完整的数据表\n",
    "print(\"\\n完整数据结果:\")\n",
    "print(df[['距离(cm)', '通道1风速(m/s)', '通道2风速(m/s)', \n",
    "          '通道1风速标准差(m/s)', '通道2风速标准差(m/s)', \n",
    "          '通道1湍流强度', '通道2湍流强度']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d23be78d-914a-4a34-8c0b-806b9f67dde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>高度(cm)</th>\n",
       "      <th>探针1风速(m/s)</th>\n",
       "      <th>探针2风速(m/s)</th>\n",
       "      <th>探针1风速标准差(m/s)</th>\n",
       "      <th>探针1湍流强度</th>\n",
       "      <th>探针2风速标准差(m/s)</th>\n",
       "      <th>探针2湍流强度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>5.260774</td>\n",
       "      <td>5.347712</td>\n",
       "      <td>0.1052</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>5.495624</td>\n",
       "      <td>5.536636</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1107</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>5.720652</td>\n",
       "      <td>5.732939</td>\n",
       "      <td>0.1144</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1147</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>5.895184</td>\n",
       "      <td>5.981946</td>\n",
       "      <td>0.1179</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1196</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>6.156944</td>\n",
       "      <td>6.192481</td>\n",
       "      <td>0.1231</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70</td>\n",
       "      <td>6.453055</td>\n",
       "      <td>6.404512</td>\n",
       "      <td>0.1291</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1281</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80</td>\n",
       "      <td>6.523379</td>\n",
       "      <td>6.453731</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1291</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>90</td>\n",
       "      <td>6.673751</td>\n",
       "      <td>6.616658</td>\n",
       "      <td>0.1335</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1323</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>6.670831</td>\n",
       "      <td>6.618308</td>\n",
       "      <td>0.1334</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.1324</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   高度(cm)  探针1风速(m/s)  探针2风速(m/s)  探针1风速标准差(m/s)  探针1湍流强度  探针2风速标准差(m/s)  \\\n",
       "0      25    5.260774    5.347712         0.1052     0.02         0.1070   \n",
       "1      30    5.495624    5.536636         0.1099     0.02         0.1107   \n",
       "2      40    5.720652    5.732939         0.1144     0.02         0.1147   \n",
       "3      50    5.895184    5.981946         0.1179     0.02         0.1196   \n",
       "4      60    6.156944    6.192481         0.1231     0.02         0.1238   \n",
       "5      70    6.453055    6.404512         0.1291     0.02         0.1281   \n",
       "6      80    6.523379    6.453731         0.1305     0.02         0.1291   \n",
       "7      90    6.673751    6.616658         0.1335     0.02         0.1323   \n",
       "8      10    6.670831    6.618308         0.1334     0.02         0.1324   \n",
       "\n",
       "   探针2湍流强度  \n",
       "0     0.02  \n",
       "1     0.02  \n",
       "2     0.02  \n",
       "3     0.02  \n",
       "4     0.02  \n",
       "5     0.02  \n",
       "6     0.02  \n",
       "7     0.02  \n",
       "8     0.02  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 数据整理\n",
    "data = {\n",
    "    '高度(cm)': [25, 30, 40, 50, 60, 70, 80, 90, 10],\n",
    "    '探针1风速(m/s)': [5.260774, 5.495624, 5.720652, 5.895184, 6.156944, 6.453055, 6.523379, 6.673751, 6.670831],\n",
    "    '探针2风速(m/s)': [5.347712, 5.536636, 5.732939, 5.981946, 6.192481, 6.404512, 6.453731, 6.616658, 6.618308]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 假设相对误差为2%\n",
    "relative_error = 0.02\n",
    "\n",
    "# 计算湍流强度\n",
    "for probe in ['探针1', '探针2']:\n",
    "    speed_col = f'{probe}风速(m/s)'\n",
    "    df[f'{probe}风速标准差(m/s)'] = df[speed_col] * relative_error\n",
    "    df[f'{probe}湍流强度'] = df[f'{probe}风速标准差(m/s)'] / df[speed_col]\n",
    "\n",
    "# 格式化结果\n",
    "df = df.round({\n",
    "    '探针1风速标准差(m/s)': 4,\n",
    "    '探针1湍流强度': 4,\n",
    "    '探针2风速标准差(m/s)': 4,\n",
    "    '探针2湍流强度': 4\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63a29025-fd1f-44ee-a5a0-9175c56b6825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "通道1风剪切指数: 0.1789\n",
      "通道2风剪切指数: 0.1613\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 假设数据存储在DataFrame中，结构与你提供的表格一致\n",
    "data = {\n",
    "    '距离(cm)': [25, 30, 40, 50, 60, 70, 80, 90, 100],\n",
    "    '通道1风速(m/s)': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速(m/s)': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61],\n",
    "    '高度(m)': [0.25, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 选择参考高度和参考风速\n",
    "z_ref = 0.25\n",
    "U_ref1 = df.loc[df['距离(cm)'] == 25, '通道1风速(m/s)'].values[0]\n",
    "U_ref2 = df.loc[df['距离(cm)'] == 25, '通道2风速(m/s)'].values[0]\n",
    "\n",
    "# 计算通道1的风剪切指数\n",
    "z = df['高度(m)'].values\n",
    "U = df['通道1风速(m/s)'].values\n",
    "log_ratio = np.log(U / U_ref1)\n",
    "log_height_ratio = np.log(z / z_ref)\n",
    "alpha1 = np.polyfit(log_height_ratio, log_ratio, 1)[0]\n",
    "\n",
    "# 计算通道2的风剪切指数\n",
    "U = df['通道2风速(m/s)'].values\n",
    "log_ratio = np.log(U / U_ref2)\n",
    "log_height_ratio = np.log(z / z_ref)\n",
    "alpha2 = np.polyfit(log_height_ratio, log_ratio, 1)[0]\n",
    "\n",
    "print(f\"通道1风剪切指数: {alpha1:.4f}\")\n",
    "print(f\"通道2风剪切指数: {alpha2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d60efd0b-5fd8-4bde-9bb7-06fb1aad1402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "参考高度 z_ref = 0.6m\n",
      "参考风速 U_ref = 6.3125m/s\n",
      "==================================================\n",
      "风剪切指数计算结果:\n",
      "通道1: α = 0.1789\n",
      "通道2: α = 0.1613\n",
      "==================================================\n",
      "幂律公式: U(z) = U_ref * (z/z_ref)^α\n",
      "其中:\n",
      "- U(z) 是高度z处的风速\n",
      "- U_ref 是参考高度z_ref处的风速\n",
      "- α 是风剪切指数\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置matplotlib支持中文显示\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 原始数据\n",
    "data = {\n",
    "    '高度(m)': [0.25, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00],\n",
    "    '通道1风速(m/s)': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速(m/s)': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 设置参考高度和参考风速\n",
    "z_ref = 0.60  # 参考高度设为60cm(0.6m)\n",
    "U_ref = 6.3125  # 参考风速设为6.3125m/s\n",
    "\n",
    "# 定义幂律函数\n",
    "def power_law(z, alpha):\n",
    "    \"\"\"风速随高度变化的幂律模型: U(z) = U_ref * (z/z_ref)^alpha\"\"\"\n",
    "    return U_ref * ((z / z_ref) ** alpha)\n",
    "\n",
    "# 计算风剪切指数（线性回归方法）\n",
    "def calculate_alpha(z, U, z_ref, U_ref):\n",
    "    \"\"\"通过对数线性回归计算风剪切指数\"\"\"\n",
    "    log_z_ratio = np.log(z / z_ref)\n",
    "    log_U_ratio = np.log(U / U_ref)\n",
    "    alpha, _ = np.polyfit(log_z_ratio, log_U_ratio, 1)\n",
    "    return alpha\n",
    "\n",
    "# 计算两个通道的风剪切指数\n",
    "alpha1 = calculate_alpha(df['高度(m)'], df['通道1风速(m/s)'], z_ref, U_ref)\n",
    "alpha2 = calculate_alpha(df['高度(m)'], df['通道2风速(m/s)'], z_ref, U_ref)\n",
    "\n",
    "# 生成拟合曲线数据\n",
    "z_fit = np.linspace(min(df['高度(m)']), max(df['高度(m)']), 100)\n",
    "U1_fit = power_law(z_fit, alpha1)\n",
    "U2_fit = power_law(z_fit, alpha2)\n",
    "\n",
    "# 绘制结果\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# 绘制原始数据点\n",
    "plt.scatter(df['高度(m)'], df['通道1风速(m/s)'], color='red', marker='o', label='通道1测量数据')\n",
    "plt.scatter(df['高度(m)'], df['通道2风速(m/s)'], color='blue', marker='s', label='通道2测量数据')\n",
    "\n",
    "# 绘制拟合曲线\n",
    "plt.plot(z_fit, U1_fit, 'r--', \n",
    "         label=f'通道1拟合: α={alpha1:.4f}, U={U_ref}·(z/{z_ref})^α')\n",
    "plt.plot(z_fit, U2_fit, 'b--', \n",
    "         label=f'通道2拟合: α={alpha2:.4f}, U={U_ref}·(z/{z_ref})^α')\n",
    "\n",
    "# 添加参考高度和参考风速的标记\n",
    "plt.axhline(y=U_ref, color='gray', linestyle=':', label=f'参考风速 U_ref={U_ref}m/s')\n",
    "plt.axvline(x=z_ref, color='gray', linestyle=':')\n",
    "plt.text(z_ref+0.01, U_ref+0.1, f'参考高度 z_ref={z_ref}m', color='gray')\n",
    "\n",
    "plt.xlabel('高度 (m)')\n",
    "plt.ylabel('风速 (m/s)')\n",
    "plt.title('风速随高度变化的幂律拟合')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 打印结果\n",
    "print(\"=\"*50)\n",
    "print(f\"参考高度 z_ref = {z_ref}m\")\n",
    "print(f\"参考风速 U_ref = {U_ref}m/s\")\n",
    "print(\"=\"*50)\n",
    "print(\"风剪切指数计算结果:\")\n",
    "print(f\"通道1: α = {alpha1:.4f}\")\n",
    "print(f\"通道2: α = {alpha2:.4f}\")\n",
    "print(\"=\"*50)\n",
    "print(\"幂律公式: U(z) = U_ref * (z/z_ref)^α\")\n",
    "print(\"其中:\")\n",
    "print(\"- U(z) 是高度z处的风速\")\n",
    "print(\"- U_ref 是参考高度z_ref处的风速\")\n",
    "print(\"- α 是风剪切指数\")\n",
    "print(\"=\"*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6eae9771-89d0-4fdb-9fb7-de28da3978ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "风速测量值与幂律模型拟合值对比表：\n",
      " 高度(m)  通道1风速(m/s)  通道1拟合风速(m/s)  通道2风速(m/s)  通道2拟合风速(m/s)\n",
      "  0.25        5.26          5.40        5.34          5.48\n",
      "  0.30        5.49          5.58        5.53          5.64\n",
      "  0.40        5.72          5.87        5.73          5.91\n",
      "  0.50        5.89          6.11        5.98          6.13\n",
      "  0.60        6.15          6.31        6.19          6.31\n",
      "  0.70        6.45          6.49        6.40          6.47\n",
      "  0.80        6.52          6.65        6.45          6.61\n",
      "  0.90        6.67          6.79        6.61          6.74\n",
      "  1.00        6.67          6.92        6.61          6.85\n",
      "\n",
      "风剪切指数计算结果:\n",
      "通道1: α = 0.1789\n",
      "通道2: α = 0.1613\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置matplotlib支持中文显示\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "\n",
    "# 原始数据\n",
    "data = {\n",
    "    '高度(m)': [0.25, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00],\n",
    "    '通道1风速(m/s)': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速(m/s)': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 设置参考高度和参考风速\n",
    "z_ref = 0.60  # 参考高度设为60cm(0.6m)\n",
    "U_ref = 6.3125  # 参考风速设为6.3125m/s\n",
    "\n",
    "# 定义幂律函数\n",
    "def power_law(z, alpha):\n",
    "    \"\"\"风速随高度变化的幂律模型: U(z) = U_ref * (z/z_ref)^alpha\"\"\"\n",
    "    return U_ref * ((z / z_ref) ** alpha)\n",
    "\n",
    "# 计算风剪切指数（线性回归方法）\n",
    "def calculate_alpha(z, U, z_ref, U_ref):\n",
    "    \"\"\"通过对数线性回归计算风剪切指数\"\"\"\n",
    "    log_z_ratio = np.log(z / z_ref)\n",
    "    log_U_ratio = np.log(U / U_ref)\n",
    "    alpha, _ = np.polyfit(log_z_ratio, log_U_ratio, 1)\n",
    "    return alpha\n",
    "\n",
    "# 计算两个通道的风剪切指数\n",
    "alpha1 = calculate_alpha(df['高度(m)'], df['通道1风速(m/s)'], z_ref, U_ref)\n",
    "alpha2 = calculate_alpha(df['高度(m)'], df['通道2风速(m/s)'], z_ref, U_ref)\n",
    "\n",
    "# 计算各高度的拟合值\n",
    "df['通道1拟合风速(m/s)'] = power_law(df['高度(m)'], alpha1)\n",
    "df['通道2拟合风速(m/s)'] = power_law(df['高度(m)'], alpha2)\n",
    "\n",
    "# 创建结果表格（保留两位小数）\n",
    "result_df = df[['高度(m)', '通道1风速(m/s)', '通道1拟合风速(m/s)', \n",
    "                '通道2风速(m/s)', '通道2拟合风速(m/s)']].copy()\n",
    "result_df = result_df.round(2)  # 保留两位小数\n",
    "\n",
    "# 显示结果表格\n",
    "print(\"风速测量值与幂律模型拟合值对比表：\")\n",
    "print(result_df.to_string(index=False))\n",
    "\n",
    "# 打印风剪切指数\n",
    "print(\"\\n风剪切指数计算结果:\")\n",
    "print(f\"通道1: α = {alpha1:.4f}\")\n",
    "print(f\"通道2: α = {alpha2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5acfd99d-acea-47a0-b4fd-7293942ca518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "通道1风剪切指数表格\n",
      "        0.25m   0.30m   0.40m   0.50m   0.60m   0.70m   0.80m 0.90m 1.00m\n",
      "0.25m       /       /       /       /       /       /       /     /     /\n",
      "0.30m  0.2347       /       /       /       /       /       /     /     /\n",
      "0.40m  0.1784  0.1427       /       /       /       /       /     /     /\n",
      "0.50m  0.1632  0.1377  0.1312       /       /       /       /     /     /\n",
      "0.60m  0.1786  0.1638  0.1788  0.2369       /       /       /     /     /\n",
      "0.70m  0.1981  0.1902  0.2146  0.2699   0.309       /       /     /     /\n",
      "0.80m  0.1846  0.1753  0.1889  0.2162  0.2031  0.0808       /     /     /\n",
      "0.90m  0.1854  0.1772  0.1895  0.2116  0.2002  0.1335  0.1931     /     /\n",
      "1.00m  0.1713  0.1617  0.1677  0.1794  0.1589   0.094  0.1019   0.0     /\n",
      "整体拟合值  0.1713       /       /       /       /       /       /     /     /\n",
      "\n",
      "通道2风剪切指数表格\n",
      "        0.25m   0.30m   0.40m   0.50m   0.60m   0.70m   0.80m 0.90m 1.00m\n",
      "0.25m       /       /       /       /       /       /       /     /     /\n",
      "0.30m  0.1918       /       /       /       /       /       /     /     /\n",
      "0.40m    0.15  0.1235       /       /       /       /       /     /     /\n",
      "0.50m  0.1633  0.1531  0.1914       /       /       /       /     /     /\n",
      "0.60m  0.1687  0.1627  0.1904  0.1893       /       /       /     /     /\n",
      "0.70m  0.1759  0.1724  0.1976  0.2017  0.2164       /       /     /     /\n",
      "0.80m  0.1624  0.1569  0.1708   0.161   0.143  0.0583       /     /     /\n",
      "0.90m  0.1666  0.1624  0.1762  0.1704  0.1619  0.1285   0.208     /     /\n",
      "1.00m  0.1539  0.1482  0.1559  0.1445  0.1285  0.0905  0.1098   0.0     /\n",
      "整体拟合值  0.1539       /       /       /       /       /       /     /     /\n",
      "\n",
      "对象类型验证：\n",
      "df1类型: <class 'pandas.core.frame.DataFrame'>\n",
      "df2类型: <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.25m</th>\n",
       "      <th>0.30m</th>\n",
       "      <th>0.40m</th>\n",
       "      <th>0.50m</th>\n",
       "      <th>0.60m</th>\n",
       "      <th>0.70m</th>\n",
       "      <th>0.80m</th>\n",
       "      <th>0.90m</th>\n",
       "      <th>1.00m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25m</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30m</th>\n",
       "      <td>0.191761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40m</th>\n",
       "      <td>0.149977</td>\n",
       "      <td>0.123496</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50m</th>\n",
       "      <td>0.163306</td>\n",
       "      <td>0.153150</td>\n",
       "      <td>0.191379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60m</th>\n",
       "      <td>0.168720</td>\n",
       "      <td>0.162660</td>\n",
       "      <td>0.190447</td>\n",
       "      <td>0.189306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70m</th>\n",
       "      <td>0.175863</td>\n",
       "      <td>0.172443</td>\n",
       "      <td>0.197604</td>\n",
       "      <td>0.201733</td>\n",
       "      <td>0.216430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80m</th>\n",
       "      <td>0.162365</td>\n",
       "      <td>0.156900</td>\n",
       "      <td>0.170764</td>\n",
       "      <td>0.160977</td>\n",
       "      <td>0.143023</td>\n",
       "      <td>0.058279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90m</th>\n",
       "      <td>0.166564</td>\n",
       "      <td>0.162383</td>\n",
       "      <td>0.176178</td>\n",
       "      <td>0.170407</td>\n",
       "      <td>0.161909</td>\n",
       "      <td>0.128467</td>\n",
       "      <td>0.208039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00m</th>\n",
       "      <td>0.153905</td>\n",
       "      <td>0.148173</td>\n",
       "      <td>0.155920</td>\n",
       "      <td>0.144505</td>\n",
       "      <td>0.128515</td>\n",
       "      <td>0.090518</td>\n",
       "      <td>0.109811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>整体拟合值</th>\n",
       "      <td>0.153905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0.25m     0.30m     0.40m     0.50m     0.60m     0.70m     0.80m  \\\n",
       "0.25m       NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "0.30m  0.191761       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "0.40m  0.149977  0.123496       NaN       NaN       NaN       NaN       NaN   \n",
       "0.50m  0.163306  0.153150  0.191379       NaN       NaN       NaN       NaN   \n",
       "0.60m  0.168720  0.162660  0.190447  0.189306       NaN       NaN       NaN   \n",
       "0.70m  0.175863  0.172443  0.197604  0.201733  0.216430       NaN       NaN   \n",
       "0.80m  0.162365  0.156900  0.170764  0.160977  0.143023  0.058279       NaN   \n",
       "0.90m  0.166564  0.162383  0.176178  0.170407  0.161909  0.128467  0.208039   \n",
       "1.00m  0.153905  0.148173  0.155920  0.144505  0.128515  0.090518  0.109811   \n",
       "整体拟合值  0.153905       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       0.90m  1.00m  \n",
       "0.25m    NaN    NaN  \n",
       "0.30m    NaN    NaN  \n",
       "0.40m    NaN    NaN  \n",
       "0.50m    NaN    NaN  \n",
       "0.60m    NaN    NaN  \n",
       "0.70m    NaN    NaN  \n",
       "0.80m    NaN    NaN  \n",
       "0.90m    NaN    NaN  \n",
       "1.00m    0.0    NaN  \n",
       "整体拟合值    NaN    NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 原始数据\n",
    "data = {\n",
    "    '高度(m)': [0.25, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00],\n",
    "    '通道1风速(m/s)': [5.26, 5.49, 5.72, 5.89, 6.15, 6.45, 6.52, 6.67, 6.67],\n",
    "    '通道2风速(m/s)': [5.34, 5.53, 5.73, 5.98, 6.19, 6.40, 6.45, 6.61, 6.61]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 计算风剪切指数的函数\n",
    "def calculate_shear_exponent(z1, z2, U1, U2):\n",
    "    \"\"\"计算两个高度之间的风剪切指数: α = ln(U2/U1) / ln(z2/z1)\"\"\"\n",
    "    return np.log(U2/U1) / np.log(z2/z1)\n",
    "\n",
    "# 创建风剪切指数表格函数\n",
    "def create_shear_dataframe(heights, speeds, channel_name):\n",
    "    \"\"\"创建风剪切指数DataFrame\"\"\"\n",
    "    n = len(heights)\n",
    "    index_labels = [f\"{h:.2f}m\" for h in heights] + [\"整体拟合值\"]\n",
    "    \n",
    "    # 初始化全NaN的DataFrame\n",
    "    columns = [f\"{h:.2f}m\" for h in heights]\n",
    "    shear_df = pd.DataFrame(np.nan, index=index_labels, columns=columns)\n",
    "    \n",
    "    # 将Pandas Series转换为NumPy数组以支持负数索引\n",
    "    heights_array = heights.values\n",
    "    speeds_array = speeds.values\n",
    "    \n",
    "    # 计算所有高度对之间的风剪切指数\n",
    "    for i in range(n):\n",
    "        for j in range(i+1, n):\n",
    "            z1, z2 = heights_array[i], heights_array[j]\n",
    "            U1, U2 = speeds_array[i], speeds_array[j]\n",
    "            shear_df.iloc[j, i] = calculate_shear_exponent(z1, z2, U1, U2)\n",
    "    \n",
    "    # 计算并添加整体拟合值（使用最低和最高高度）\n",
    "    alpha = calculate_shear_exponent(heights_array[0], heights_array[-1], \n",
    "                                    speeds_array[0], speeds_array[-1])\n",
    "    shear_df.loc[\"整体拟合值\", columns[0]] = alpha\n",
    "    \n",
    "    # 添加通道名称作为表格标题\n",
    "    shear_df.name = f\"{channel_name}风剪切指数表格\"\n",
    "    \n",
    "    return shear_df\n",
    "\n",
    "# 生成两个独立的DataFrame\n",
    "df1 = create_shear_dataframe(df['高度(m)'], df['通道1风速(m/s)'], \"通道1\")\n",
    "df2 = create_shear_dataframe(df['高度(m)'], df['通道2风速(m/s)'], \"通道2\")\n",
    "\n",
    "# 显示结果（保留四位小数）\n",
    "print(df1.name)\n",
    "print(df1.round(4).fillna('/').to_string())\n",
    "\n",
    "print(\"\\n\" + df2.name)\n",
    "print(df2.round(4).fillna('/').to_string())\n",
    "\n",
    "# 验证对象类型\n",
    "print(\"\\n对象类型验证：\")\n",
    "print(f\"df1类型: {type(df1)}\")\n",
    "print(f\"df2类型: {type(df2)}\")\n",
    "df1\n",
    "df2"
   ]
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   "source": []
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